Model Analysis
Nikhil Gupta
2019-02-12 23:05:46
sessionInfo()## R version 3.5.1 (2018-07-02)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 17134)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] compiler_3.5.1 magrittr_1.5 tools_3.5.1 htmltools_0.3.6
## [5] yaml_2.2.0 Rcpp_1.0.0 stringi_1.2.4 rmarkdown_1.11
## [9] knitr_1.20 stringr_1.3.1 digest_0.6.18 evaluate_0.12User Inputs
output.var = params$output.var
transform.abs = params$transform.abs
log.pred = params$log.pred
eda = params$eda
algo.forward = params$algo.forward
algo.backward = params$algo.backward
algo.stepwise = params$algo.stepwise
algo.LASSO = params$algo.LASSO
algo.LARS = params$algo.LARS
algo.forward.caret = params$algo.forward.caret
algo.backward.caret = params$algo.backward.caret
algo.stepwise.caret = params$algo.stepwise.caret
algo.LASSO.caret = params$algo.LASSO.caret
algo.LARS.caret = params$algo.LARS.caret
message("Parameters used for training/prediction: ")## Parameters used for training/prediction:str(params)## List of 14
## $ output.var : chr "y3"
## $ transform.abs : logi FALSE
## $ log.pred : logi FALSE
## $ eda : logi FALSE
## $ algo.forward : logi FALSE
## $ algo.backward : logi FALSE
## $ algo.stepwise : logi FALSE
## $ algo.LASSO : logi FALSE
## $ algo.LARS : logi FALSE
## $ algo.forward.caret : logi TRUE
## $ algo.backward.caret: logi TRUE
## $ algo.stepwise.caret: logi TRUE
## $ algo.LASSO.caret : logi TRUE
## $ algo.LARS.caret : logi TRUE# Setup Labels
# alt.scale.label.name = Alternate Scale variable name
# - if predicting on log, then alt.scale is normal scale
# - if predicting on normal scale, then alt.scale is log scale
if (log.pred == TRUE){
label.names = paste('log.',output.var,sep="")
alt.scale.label.name = output.var
}
if (log.pred == FALSE){
label.names = output.var
alt.scale.label.name = paste('log.',output.var,sep="")
}Prepare Data
Read and Clean Features
features = read.csv("../../Data/features.csv")
features.highprec = read.csv("../../Data/features_highprec.csv")all.equal(features, features.highprec)## [1] "Component \"x11\": Mean relative difference: 0.001401482"
## [2] "Component \"stat9\": Mean relative difference: 0.0002946299"
## [3] "Component \"stat12\": Mean relative difference: 0.0005151515"
## [4] "Component \"stat13\": Mean relative difference: 0.001354369"
## [5] "Component \"stat18\": Mean relative difference: 0.0005141104"
## [6] "Component \"stat22\": Mean relative difference: 0.001135977"
## [7] "Component \"stat25\": Mean relative difference: 0.0001884615"
## [8] "Component \"stat29\": Mean relative difference: 0.001083691"
## [9] "Component \"stat36\": Mean relative difference: 0.00021513"
## [10] "Component \"stat37\": Mean relative difference: 0.0004578125"
## [11] "Component \"stat43\": Mean relative difference: 0.0003473684"
## [12] "Component \"stat45\": Mean relative difference: 0.0002951699"
## [13] "Component \"stat46\": Mean relative difference: 0.0009745763"
## [14] "Component \"stat47\": Mean relative difference: 8.829902e-05"
## [15] "Component \"stat55\": Mean relative difference: 0.001438066"
## [16] "Component \"stat57\": Mean relative difference: 0.0001056911"
## [17] "Component \"stat58\": Mean relative difference: 0.0004882261"
## [18] "Component \"stat60\": Mean relative difference: 0.0002408377"
## [19] "Component \"stat62\": Mean relative difference: 0.0004885106"
## [20] "Component \"stat66\": Mean relative difference: 1.73913e-06"
## [21] "Component \"stat67\": Mean relative difference: 0.0006265823"
## [22] "Component \"stat73\": Mean relative difference: 0.003846154"
## [23] "Component \"stat75\": Mean relative difference: 0.002334906"
## [24] "Component \"stat83\": Mean relative difference: 0.0005628415"
## [25] "Component \"stat86\": Mean relative difference: 0.0006104418"
## [26] "Component \"stat94\": Mean relative difference: 0.001005115"
## [27] "Component \"stat97\": Mean relative difference: 0.0003551913"
## [28] "Component \"stat98\": Mean relative difference: 0.0006157635"
## [29] "Component \"stat106\": Mean relative difference: 0.0008267717"
## [30] "Component \"stat109\": Mean relative difference: 0.0005121359"
## [31] "Component \"stat110\": Mean relative difference: 0.0007615527"
## [32] "Component \"stat111\": Mean relative difference: 0.001336134"
## [33] "Component \"stat114\": Mean relative difference: 7.680492e-05"
## [34] "Component \"stat117\": Mean relative difference: 0.0002421784"
## [35] "Component \"stat122\": Mean relative difference: 0.0006521084"
## [36] "Component \"stat123\": Mean relative difference: 8.333333e-05"
## [37] "Component \"stat125\": Mean relative difference: 0.002385135"
## [38] "Component \"stat130\": Mean relative difference: 0.001874016"
## [39] "Component \"stat132\": Mean relative difference: 0.0003193182"
## [40] "Component \"stat135\": Mean relative difference: 0.0001622517"
## [41] "Component \"stat136\": Mean relative difference: 7.722008e-05"
## [42] "Component \"stat138\": Mean relative difference: 0.0009739953"
## [43] "Component \"stat143\": Mean relative difference: 0.0004845361"
## [44] "Component \"stat146\": Mean relative difference: 0.0005821596"
## [45] "Component \"stat148\": Mean relative difference: 0.0005366922"
## [46] "Component \"stat153\": Mean relative difference: 0.0001557522"
## [47] "Component \"stat154\": Mean relative difference: 0.001351916"
## [48] "Component \"stat157\": Mean relative difference: 0.0005427928"
## [49] "Component \"stat162\": Mean relative difference: 0.002622951"
## [50] "Component \"stat167\": Mean relative difference: 0.0005905172"
## [51] "Component \"stat168\": Mean relative difference: 0.0002791096"
## [52] "Component \"stat169\": Mean relative difference: 0.0004121827"
## [53] "Component \"stat170\": Mean relative difference: 0.0004705882"
## [54] "Component \"stat174\": Mean relative difference: 0.0003822894"
## [55] "Component \"stat179\": Mean relative difference: 0.0008286604"
## [56] "Component \"stat184\": Mean relative difference: 0.0007526718"
## [57] "Component \"stat187\": Mean relative difference: 0.0005122768"
## [58] "Component \"stat193\": Mean relative difference: 4.215116e-05"
## [59] "Component \"stat199\": Mean relative difference: 0.002155844"
## [60] "Component \"stat203\": Mean relative difference: 0.0003738318"
## [61] "Component \"stat213\": Mean relative difference: 0.000667676"
## [62] "Component \"stat215\": Mean relative difference: 0.0003997955"head(features)## JobName x1 x2 x3 x4 x5 x6
## 1 Job_00001 2.0734508 4.917267 19.96188 3.520878 7.861051 1.6067589
## 2 Job_00002 2.2682543 4.955773 19.11939 19.763031 6.931355 1.3622041
## 3 Job_00003 1.7424456 2.059819 13.37912 38.829132 6.274053 2.0529845
## 4 Job_00004 0.7873555 2.613983 17.23044 64.402557 5.377652 0.9067419
## 5 Job_00005 2.3342753 4.299076 14.64883 52.537304 6.793368 2.4605792
## 6 Job_00006 1.2365089 2.795370 11.13127 96.819939 6.583971 2.3510606
## x7 x8 x9 x10 x11 x12 x13
## 1 2.979479 8.537228 1.103368 4.6089458 1.05e-07 7.995825 13.215498
## 2 2.388119 6.561461 0.588572 1.0283282 1.03e-07 7.486966 22.557224
## 3 2.043592 10.275595 4.834385 4.3872848 1.06e-07 6.350142 15.049810
## 4 2.395118 13.487331 3.340190 4.5053501 9.47e-08 9.548698 17.170635
## 5 2.891535 9.362389 1.246039 1.7333300 1.01e-07 9.596095 5.794567
## 6 1.247838 7.033354 1.852231 0.4839371 1.07e-07 3.810983 23.863169
## x14 x15 x16 x17 x18 x19 x20
## 1 4.377983 0.2370623 6.075459 3.988347 4.767475 2.698775 1.035893
## 2 2.059315 0.5638121 6.903891 4.152054 6.849232 9.620731 1.915288
## 3 3.260057 2.0603445 8.424065 4.489893 3.493591 4.715386 1.558103
## 4 3.093478 1.8806034 11.189792 2.134271 5.588357 5.107871 1.489588
## 5 3.943076 1.5820830 7.096742 3.563378 7.765610 1.360272 1.240283
## 6 1.280562 1.1733382 7.062051 1.341864 7.748325 5.009365 1.725179
## x21 x22 x23 stat1 stat2 stat3 stat4
## 1 42.36548 1.356213 2.699796 2.3801832 0.1883335 -1.2284011 -0.5999233
## 2 26.63295 4.053961 2.375127 -1.4069480 1.8140973 1.6204884 2.6422672
## 3 20.09693 3.079888 4.488420 -0.7672566 -0.1230289 1.1415752 2.9805934
## 4 32.60415 1.355396 3.402398 0.4371202 -1.9355906 0.9028624 -1.6025400
## 5 44.58361 1.940301 2.249011 2.4492466 -0.6172000 -2.5520642 -2.1485929
## 6 28.75102 2.500499 5.563972 -1.7899084 1.8853619 2.4154840 -2.6022179
## stat5 stat6 stat7 stat8 stat9 stat10
## 1 0.148893163 -0.6622978 -2.4851868 0.3647782 2.5364335 2.92067981
## 2 1.920768980 1.7411555 -1.9599979 -2.0190558 -1.3732762 -0.31642506
## 3 2.422584300 -0.4166040 2.2205689 -2.6741531 0.4844292 2.73379230
## 4 -0.001795933 -0.6946563 -0.3693534 -0.9709467 1.7960306 0.74771154
## 5 -2.311132430 -1.0166832 2.7269876 1.5424492 -1.3156369 -0.09767897
## 6 -1.785491470 -1.8599915 1.4875095 2.0188572 -1.4892503 -1.41103566
## stat11 stat12 stat13 stat14 stat15 stat16
## 1 -2.3228905 -2.480567 -0.6335157 -0.3650149 -0.5322812 0.6029300
## 2 -0.8547903 1.119316 0.7227427 0.2121097 -0.1452281 -2.0361528
## 3 -2.1821580 2.865401 -2.9756081 2.9871745 1.9539525 -1.8857163
## 4 1.3982378 1.856765 -1.0379983 2.3341896 2.3057184 -2.8947697
## 5 0.9567220 2.567549 0.3184886 1.0307668 0.1644241 -0.6613821
## 6 0.5341771 -1.461822 0.4402476 -1.9282095 -0.3680157 1.8188807
## stat17 stat18 stat19 stat20 stat21 stat22
## 1 -1.04516208 2.3544915 2.4049001 0.2633883 -0.9788178 1.7868229
## 2 0.09513074 0.4727738 1.8899702 2.7892542 -1.3919091 -1.7198164
## 3 0.40285346 1.4655282 -1.4952788 2.9162340 -2.3893208 2.8161423
## 4 2.97446084 2.3896182 2.3083484 -1.1894441 -2.1982553 1.3666242
## 5 -0.98465055 0.6900643 1.5894209 -2.1204538 1.7961155 -0.9362189
## 6 -1.45726359 -2.1139548 -0.3964904 1.1764175 -2.9100556 -2.1359294
## stat23 stat24 stat25 stat26 stat27 stat28
## 1 -2.3718851 2.8580718 -0.4719713 -2.817086 -0.9518474 2.88892484
## 2 -2.3293245 1.5577759 -1.9569720 1.554194 -0.5081459 -1.58715141
## 3 -2.5402296 0.1422861 0.3572798 -1.051886 -2.1541717 0.03074004
## 4 -1.9679050 -1.4077642 2.5097435 1.683121 -0.2549745 -2.90384054
## 5 2.0523429 -2.2084844 -1.9280857 -2.116736 1.8180779 -1.42167580
## 6 0.2184991 -0.7599817 2.6880329 -2.903350 -1.0733233 -2.92416644
## stat29 stat30 stat31 stat32 stat33 stat34
## 1 0.7991088 -2.0059092 -0.2461502 0.6482101 -2.87462163 -0.3601543
## 2 1.9758110 -0.3874187 1.3566630 2.6493473 2.28463054 1.8591728
## 3 -0.4460218 1.0279679 1.3998452 -1.0183365 1.41109037 -2.4183984
## 4 1.0571996 2.5588036 -2.9830337 -1.1299983 0.05470414 -1.5566561
## 5 0.8854889 2.2774174 2.6499031 2.3053405 -2.39148426 -1.8272992
## 6 -0.8405267 0.1311945 0.4321289 -2.9622040 -2.55387473 2.6396458
## stat35 stat36 stat37 stat38 stat39 stat40
## 1 2.4286051 -0.5420244 -2.6782637 -2.8874269 -0.8945006 1.1749642
## 2 1.3709245 -1.3714181 1.3901204 1.2273489 -0.8934880 1.0540369
## 3 -0.9805572 2.0571353 0.8845031 2.0574493 1.1222047 1.8528618
## 4 1.0969149 -2.2820673 1.8852408 0.5391517 2.7334342 -0.4372566
## 5 -1.0971669 1.4867796 -2.3738465 -0.3743561 1.4266498 1.2551680
## 6 0.4584349 -2.2696617 -0.9935142 -0.5350499 -0.7874799 2.0009417
## stat41 stat42 stat43 stat44 stat45 stat46
## 1 -1.0474428 -1.3909023 2.54110503 -1.4320793 0.6298335 -2.09296608
## 2 2.5380247 1.6476108 0.44128850 -2.5049477 1.2726039 1.72492969
## 3 1.1477574 0.2288794 0.08891252 2.3044751 -0.7735722 -0.07302936
## 4 -1.3808300 -2.7900956 2.38297582 0.1686397 -2.1591296 1.60828602
## 5 0.2257536 1.9542116 2.66429019 0.8026123 -1.5521187 1.61751962
## 6 -1.3364114 -2.2898803 2.80735397 -0.8413086 1.0057797 -1.50653386
## stat47 stat48 stat49 stat50 stat51 stat52
## 1 -2.8318939 2.1445766 0.5668035 0.1544579 0.6291955 2.2197027
## 2 -0.5804687 -1.3689737 1.4908396 1.2465997 0.8896304 -2.6024318
## 3 0.7918019 1.5712964 1.1038082 -0.2545658 -2.1662638 0.2660159
## 4 -1.8894132 0.5680230 -0.7023218 -0.3972188 0.1578027 2.1770194
## 5 2.1088455 -2.7195437 2.1961412 -0.2615084 1.2109556 0.8260623
## 6 -1.4400891 -0.9421459 -1.7324599 -2.1720727 -2.8129435 0.6958785
## stat53 stat54 stat55 stat56 stat57 stat58
## 1 2.176805 0.5546907 -2.19704103 -0.2884173 1.3232913 -1.32824039
## 2 -2.107441 1.3864788 0.08781975 1.9998228 0.8014438 -0.26979154
## 3 1.234197 2.1337581 1.65231645 -0.4388691 -0.1811156 2.11277962
## 4 2.535406 -2.1387620 0.12856023 -1.9906180 0.9626449 1.65232646
## 5 -2.457080 2.1633499 0.60441124 2.5449364 -1.4978440 2.60542655
## 6 2.003033 -0.5379940 -2.19647264 -1.1954677 -0.5974466 -0.04703835
## stat59 stat60 stat61 stat62 stat63 stat64
## 1 1.24239659 -2.5798278 1.327928 1.68560362 0.6284891 -1.6798652
## 2 0.06379301 0.9465770 1.116928 0.03128772 -2.1944375 0.3382609
## 3 0.93223447 2.4597080 0.465251 -1.71033382 -0.5156728 1.8276784
## 4 -0.29840910 0.7273473 -2.313066 -1.47696018 2.5910559 -1.5127999
## 5 -1.17610002 -1.7948418 -2.669305 0.17813617 2.8956099 2.9411416
## 6 -1.01793981 0.2817057 2.228023 -0.86494124 -0.9747949 -0.1569053
## stat65 stat66 stat67 stat68 stat69 stat70
## 1 -2.9490898 -0.3325469 1.5745990 -2.2978280 1.5451891 -1.345990
## 2 -1.1174885 -1.5728682 -2.9229002 0.2658547 -1.9616533 2.506130
## 3 -0.2231264 -0.4503301 0.7932286 -1.2453773 -2.2309763 2.309761
## 4 -0.3522418 -2.0720532 0.9442933 2.9212906 0.5100371 -2.441108
## 5 -2.1648991 1.2002029 2.8266985 0.7461294 1.6772674 -1.280000
## 6 -2.2295458 1.1446493 0.2024925 -0.2983998 -2.8203752 1.224030
## stat71 stat72 stat73 stat74 stat75 stat76
## 1 1.0260956 2.1071210 2.6625669 -2.8924677 -0.02132523 -2.5058765
## 2 0.3525076 1.6922342 -1.2167022 -1.7271879 2.21176434 1.9329683
## 3 -2.1799035 -2.2645276 0.1415582 0.9887453 1.95592320 0.2951785
## 4 -2.4051409 2.0876484 -0.8632146 0.4011389 -1.16986716 -1.2391174
## 5 1.3538754 -0.8089395 -0.5122626 -2.1696892 1.07344925 2.6696169
## 6 -2.8073371 -1.4450488 0.5481212 -1.4381690 0.80917043 -0.1365944
## stat77 stat78 stat79 stat80 stat81 stat82
## 1 -2.5631845 -2.40331340 0.38416120 -1.2564875 -0.1550840 -1.1762617
## 2 -0.4462085 0.38400793 1.80483031 -0.8387642 0.7624431 0.9936900
## 3 1.6757870 -1.81900752 2.70904708 -0.3201959 2.5754235 1.6346260
## 4 -2.1012006 -2.24691081 1.78056848 1.0323739 1.0762523 2.1343851
## 5 -2.5736733 -1.99958372 -0.05388495 -2.5630073 -2.8783002 -0.5752426
## 6 1.6143972 0.03233746 2.90835762 1.4000487 2.9275615 -2.8503830
## stat83 stat84 stat85 stat86 stat87 stat88
## 1 1.2840565 -2.6794965 1.3956039 -1.5290235 2.221152 2.3794982
## 2 -0.2380048 1.9314318 -1.6747955 -0.3663656 1.582659 -0.5222489
## 3 -0.9150769 -1.5520337 2.4186287 2.7273662 1.306642 0.1320062
## 4 -2.5824408 -2.7775943 0.5085060 0.4689015 2.053348 0.7957955
## 5 -1.0017741 -0.2009138 0.3770109 2.4335201 -1.118058 1.3953410
## 6 2.4891765 2.9931953 -1.4171852 0.3905659 -1.856119 -2.1690490
## stat89 stat90 stat91 stat92 stat93 stat94
## 1 -0.9885110 -0.8873261 -2.7810929 -1.53325891 2.6002395 1.8890998
## 2 0.9982028 -1.2382015 -0.1574496 0.41086048 -0.5412626 -0.2421387
## 3 0.5956759 1.6871066 2.2452753 2.74279594 -1.5860478 2.9393122
## 4 2.0902634 2.1752586 -2.0677712 -2.37861037 1.1653302 0.1500632
## 5 2.9820614 0.8111660 -0.7842287 0.03766387 -1.1681970 2.1217251
## 6 -1.7428021 0.1579032 1.7456742 -0.36858466 -0.1304616 -1.4555819
## stat95 stat96 stat97 stat98 stat99 stat100
## 1 -2.6056035 -0.5814857 2.57652426 -2.3297751 2.6324007 1.445827
## 2 -2.0271583 -0.9126074 2.49582648 0.9745382 1.1339203 -2.549544
## 3 0.3823181 -0.6324139 2.46221566 1.1151560 0.4624891 0.107072
## 4 2.6414623 -0.6630505 2.10394859 1.2627635 0.4861740 1.697012
## 5 1.4642254 2.6485956 -0.07699547 0.6219473 -1.8815142 -2.685463
## 6 1.8937331 -0.4690555 1.04671776 -0.5879866 -0.9766789 2.405940
## stat101 stat102 stat103 stat104 stat105 stat106
## 1 -2.1158021 2.603936 1.7745128 -1.8903574 -1.8558655 1.0122044
## 2 -2.7998588 -2.267895 0.5336456 -0.2859477 -0.5196246 -0.9417582
## 3 0.7969509 -1.744906 -0.7960327 1.9767258 -0.2007264 -0.7872376
## 4 1.7071959 -1.540221 1.6770362 1.5395796 -0.4855365 -1.2894115
## 5 -1.4627420 -1.700983 2.4376490 0.2731541 1.5275587 1.3256483
## 6 2.6888530 1.090155 2.0769854 1.9615480 1.8689761 2.8975825
## stat107 stat108 stat109 stat110 stat111 stat112
## 1 1.954508 -0.3376471 2.503084 0.3099165 2.7209847 -2.3911204
## 2 -2.515160 0.3998704 -1.077093 2.4228268 -0.7759693 0.2513882
## 3 1.888827 1.5819857 -2.066659 -2.0008364 0.6997684 2.6157095
## 4 1.076395 -1.8524148 -2.689204 1.0985872 1.2389493 2.1018629
## 5 2.828866 -1.8590252 -2.424163 1.4391942 -0.6173239 -1.5218846
## 6 -1.419639 0.7888914 1.996463 0.9813507 0.9034198 1.3810679
## stat113 stat114 stat115 stat116 stat117 stat118
## 1 -1.616161 1.0878664 0.9860094 -0.06288462 -1.013501 -1.2212842
## 2 -1.554771 1.8683100 0.4880588 -0.63865489 -1.610217 -1.7713343
## 3 -2.679801 -2.9486952 1.7753417 0.90311784 -1.318836 -0.1429040
## 4 2.459229 -0.5584171 0.4419581 -0.09586351 0.595442 0.2883342
## 5 -2.102200 1.6300170 -2.3498287 1.36771894 -1.912202 -0.2563821
## 6 -1.835037 0.6577786 -2.9928374 2.13540316 -1.437299 -0.9570006
## stat119 stat120 stat121 stat122 stat123 stat124
## 1 2.9222729 1.9151262 1.6686068 2.0061224 1.5723072 0.78819227
## 2 2.1828208 0.8283178 -2.4458632 1.7133740 1.1393738 -0.07182054
## 3 0.9721319 1.2723130 2.8002086 2.7670381 -2.2252586 2.17499113
## 4 -1.9327896 -2.5369370 1.7835028 1.0262097 -1.8790983 -0.43639564
## 5 1.3230809 -2.8145256 -0.9547533 -2.0435417 -0.2758764 -1.85668027
## 6 0.1720700 -1.4568460 1.4115051 -0.9878145 2.3895061 -2.33730745
## stat125 stat126 stat127 stat128 stat129 stat130
## 1 1.588372 1.1620011 -0.2474264 1.650328 2.5147598 0.37283245
## 2 -1.173771 0.8162020 0.3510315 -1.263667 1.7245284 -0.72852904
## 3 -1.503497 -0.5656394 2.8040256 -2.139287 -1.7221642 2.17899609
## 4 1.040967 -2.9039600 0.3103742 1.462339 -1.2940350 -2.95015502
## 5 -2.866184 1.6885070 -2.2525666 -2.628631 1.8581577 2.80127025
## 6 -1.355111 1.5017927 0.4295921 -0.580415 0.9851009 -0.03773117
## stat131 stat132 stat133 stat134 stat135 stat136
## 1 -0.09028241 0.5194538 2.8478346 2.6664724 -2.0206311 1.398415090
## 2 -0.53045595 1.4134049 2.9180586 0.3299096 1.4784122 -1.278896090
## 3 1.35843194 0.2279946 0.3532595 0.6138676 -0.3443284 0.057763811
## 4 -1.92450273 1.2698178 -1.5299660 -2.6083462 1.1665530 -0.187791914
## 5 1.49036849 2.6337729 -2.3206244 0.4978287 -1.7397571 0.001200184
## 6 -0.64642709 -1.9256228 1.7032650 -0.9152725 -0.3188055 2.155395980
## stat137 stat138 stat139 stat140 stat141 stat142
## 1 -1.2794871 0.4064890 -0.4539998 2.6660173 -1.8375313 0.4711883
## 2 -2.7709017 -1.6303773 -1.9025910 0.2572918 0.6612002 1.4764348
## 3 -1.1930757 -0.1051243 -0.5108380 -1.0879666 2.4969513 -0.9477230
## 4 -1.2318919 2.2348571 0.1788580 -1.5851788 -1.2384283 -2.1859181
## 5 1.8685058 2.7229517 -2.9077182 2.6606939 -1.5963592 -2.2213492
## 6 -0.4807318 -1.2117369 -0.9358531 -2.5100758 -2.3803916 -0.7096854
## stat143 stat144 stat145 stat146 stat147 stat148
## 1 1.9466263 2.2689433 -0.3597288 -0.6551386 1.65438592 0.6404466
## 2 1.3156421 2.4459090 -0.3790028 1.4858465 -0.07784461 1.0096149
## 3 0.1959563 2.3062942 1.8459278 2.6848175 -2.70935774 -1.2093409
## 4 1.7633296 -2.8171508 2.0902622 -2.6625464 -1.12600601 -2.1926479
## 5 0.3885758 1.8160636 2.8257299 -1.4526173 1.60679603 2.3807991
## 6 0.7623450 0.2692145 -2.4307463 -2.1244523 -2.67803812 -1.5273387
## stat149 stat150 stat151 stat152 stat153 stat154
## 1 0.1583575 0.4755351 0.3213410 2.0241520 1.5720103 -0.1825875
## 2 -0.4311406 2.9577663 0.6937252 0.1397280 0.3775735 -1.1012636
## 3 -0.8352824 2.5716205 1.7528236 0.4326277 -2.2334397 -2.6265771
## 4 -2.8069143 1.8813509 2.3358023 0.1015632 1.2117474 -1.3714278
## 5 -1.6166265 1.1112266 -1.1998471 2.9316769 -2.1676455 -0.3411089
## 6 -0.2265472 2.7264354 -1.6746094 -2.3376281 -1.7022788 -1.2352397
## stat155 stat156 stat157 stat158 stat159 stat160
## 1 -1.139657 0.07061254 0.5893906 -1.9920996 -2.83714366 2.249398
## 2 -2.041093 0.74047768 2.5415072 -1.2697256 -1.64364433 -2.448922
## 3 -1.219507 -0.55198693 0.4046920 1.2098547 -0.90412390 -1.934093
## 4 2.992191 2.33222485 2.0622969 -0.6714653 2.76836085 -1.431120
## 5 -2.362356 -1.23906672 0.4746319 -0.7849202 0.69399995 2.052411
## 6 -1.604499 1.31051409 -0.5164744 0.6288667 0.07899523 -2.287402
## stat161 stat162 stat163 stat164 stat165 stat166
## 1 1.7182635 -1.2323593 2.7350423 1.0707235 1.1621544 0.9493989
## 2 -0.6247674 2.6740098 2.8211024 1.5561292 -1.1027147 1.0519739
## 3 -0.6230453 -0.7993517 -2.8318374 -1.1148673 1.4261659 0.5294309
## 4 1.7644744 0.1696584 1.2653207 0.6621516 0.9470508 0.1985014
## 5 -1.2070210 0.7243784 0.9736322 2.7426259 -2.6862383 1.6840212
## 6 2.3705316 -2.1667893 -0.2516685 -0.8425958 -1.9099342 -2.8607297
## stat167 stat168 stat169 stat170 stat171 stat172
## 1 0.1146510 2.3872008 1.1180918 -0.95370555 -2.25076509 0.2348182
## 2 1.0760417 -2.0449336 0.9715676 -0.40173489 -0.11953555 -2.3107369
## 3 1.1735898 1.3860190 -2.2894719 0.06350347 0.29191551 -1.6079744
## 4 2.5511832 0.5446648 1.2694012 -0.84571201 0.79789722 0.2623538
## 5 2.2900002 2.6289782 -0.2783571 1.39032829 -0.55532032 1.0499046
## 6 -0.7513983 2.9617066 -2.2119520 -1.71958113 -0.01452018 -0.2751517
## stat173 stat174 stat175 stat176 stat177 stat178
## 1 1.79366076 -1.920206 -0.38841942 0.8530301 1.64532077 -1.1354179
## 2 -0.07484659 1.337846 2.20911694 0.9616837 -2.80810070 -2.1136749
## 3 -1.05521810 -1.483741 0.06148359 2.3066039 -0.34688616 1.1840581
## 4 0.31460321 1.195741 2.97633862 1.1685091 -0.06346265 1.4205489
## 5 -1.39428365 2.458523 0.64836472 -1.0396386 -0.57828104 -0.5006818
## 6 2.31844401 1.239864 -2.06490874 0.7696204 -1.77586019 2.0855925
## stat179 stat180 stat181 stat182 stat183 stat184
## 1 2.0018647 0.1476815 -1.27279520 1.9181504 -0.5297624 -2.9718938
## 2 -2.1351449 2.9012582 -1.09914911 -2.5488517 -2.8377736 1.4073374
## 3 -1.7819908 2.9902627 0.81908613 0.2503852 0.3712984 -2.1714024
## 4 -0.1026974 -2.4763253 -2.52645421 1.3096315 2.1458161 -1.5228094
## 5 -2.2298794 2.4465680 -0.70346898 -1.6997617 2.9178164 -0.3615532
## 6 -1.1168108 1.5552123 -0.01361342 1.7338791 -1.1104763 0.1882416
## stat185 stat186 stat187 stat188 stat189 stat190
## 1 -0.1043832 -1.5047463 2.700351 -2.4780862 -1.9078265 0.9978108
## 2 -2.0310574 -0.5380074 -1.963275 -1.2221278 -2.4290681 -1.9515115
## 3 2.6727278 1.2688179 -1.399018 -2.9612138 2.6456394 2.0073323
## 4 -2.7796295 2.0682354 2.243727 0.4296881 0.1931333 2.2710960
## 5 -0.6231265 2.5833981 2.229041 0.8139584 1.4544131 1.8886451
## 6 2.7204690 -2.4469144 -1.421998 1.7477882 -0.1481806 0.6011560
## stat191 stat192 stat193 stat194 stat195 stat196
## 1 -0.6644351 2.6270833 -1.1094601 -2.4200392 2.870713 -0.6590932
## 2 -0.6483142 1.4519118 -0.1963493 -2.3025322 1.255608 2.1617947
## 3 -1.5457382 -0.2977442 -1.7045015 0.7962404 -1.696063 -1.4771117
## 4 -1.1780495 -2.9747574 -1.1471518 -1.2377013 -1.010672 -2.6055975
## 5 2.8813178 -1.8964081 -1.2653487 -1.7839754 -2.872581 2.3033464
## 6 0.4437973 0.6599325 -1.4029555 -2.3118258 -1.792232 1.3934380
## stat197 stat198 stat199 stat200 stat201 stat202
## 1 -0.83056986 0.9550526 -1.7025776 -2.8263099 -0.7023998 0.2272806
## 2 -1.42178249 -1.2471864 2.5723093 -0.0233496 -1.8975239 1.9472262
## 3 -0.19233958 -0.5161456 0.0279946 -1.2333704 -2.9672263 -2.8666208
## 4 -1.23145902 1.4728470 -0.4562025 -2.2983441 -1.5101184 0.2530525
## 5 1.85018563 -1.8269292 -0.6337969 -2.1473246 0.9909850 1.0950903
## 6 -0.09311061 0.5144456 -2.8178268 -2.7555969 -2.3546004 -1.0558939
## stat203 stat204 stat205 stat206 stat207 stat208
## 1 1.166631220 0.007453276 2.9961641 1.5327307 -2.2293356 -0.9946009
## 2 -0.235396504 2.132749800 0.3707606 1.5604026 -1.0089217 2.1474257
## 3 0.003180946 2.229793310 2.7354040 0.8992231 2.9694967 2.3081024
## 4 -0.474482715 -1.584772230 -2.3224132 -0.9409741 -2.3179255 0.8032548
## 5 2.349412920 -1.276320220 -2.0203719 -1.1733509 1.0371852 -2.5086207
## 6 0.727436960 -0.960191786 -0.8964998 -1.6406623 -0.2330488 1.7993879
## stat209 stat210 stat211 stat212 stat213 stat214
## 1 -2.2182105 -1.4099774 -1.656754 2.6602585 -2.9270992 1.1240714
## 2 -2.8932488 -1.1641679 -2.605423 -1.5650513 2.9523673 2.0266318
## 3 -1.8279589 0.0472350 -2.026734 2.5054367 0.9903042 0.3274105
## 4 -1.0878067 0.1171303 2.645891 -1.6775225 1.3452160 1.4694063
## 5 -0.8158175 0.4060950 0.912256 0.2925677 2.1610141 0.5679936
## 6 -2.2664354 -0.2061083 -1.435174 2.6645632 0.4216259 -0.6419122
## stat215 stat216 stat217
## 1 -2.7510750 -0.5501796 1.2638469
## 2 2.8934650 -2.4099574 -1.2411407
## 3 -1.0947676 1.2852937 1.5411530
## 4 0.6343777 0.1345372 2.9102673
## 5 0.9908702 1.7909757 -2.0902610
## 6 -2.8113887 -1.0624912 0.2765074head(features.highprec)## JobName x1 x2 x3 x4 x5 x6
## 1 Job_00001 2.0734508 4.917267 19.96188 3.520878 7.861051 1.6067589
## 2 Job_00002 2.2682543 4.955773 19.11939 19.763031 6.931355 1.3622041
## 3 Job_00003 1.7424456 2.059819 13.37912 38.829132 6.274053 2.0529845
## 4 Job_00004 0.7873555 2.613983 17.23044 64.402557 5.377652 0.9067419
## 5 Job_00005 2.3342753 4.299076 14.64883 52.537304 6.793368 2.4605792
## 6 Job_00006 1.2365089 2.795370 11.13127 96.819939 6.583971 2.3510606
## x7 x8 x9 x10 x11 x12 x13
## 1 2.979479 8.537228 1.103368 4.6089458 1.050025e-07 7.995825 13.215498
## 2 2.388119 6.561461 0.588572 1.0283282 1.034518e-07 7.486966 22.557224
## 3 2.043592 10.275595 4.834385 4.3872848 1.062312e-07 6.350142 15.049810
## 4 2.395118 13.487331 3.340190 4.5053501 9.471887e-08 9.548698 17.170635
## 5 2.891535 9.362389 1.246039 1.7333300 1.010552e-07 9.596095 5.794567
## 6 1.247838 7.033354 1.852231 0.4839371 1.071662e-07 3.810983 23.863169
## x14 x15 x16 x17 x18 x19 x20
## 1 4.377983 0.2370623 6.075459 3.988347 4.767475 2.698775 1.035893
## 2 2.059315 0.5638121 6.903891 4.152054 6.849232 9.620731 1.915288
## 3 3.260057 2.0603445 8.424065 4.489893 3.493591 4.715386 1.558103
## 4 3.093478 1.8806034 11.189792 2.134271 5.588357 5.107871 1.489588
## 5 3.943076 1.5820830 7.096742 3.563378 7.765610 1.360272 1.240283
## 6 1.280562 1.1733382 7.062051 1.341864 7.748325 5.009365 1.725179
## x21 x22 x23 stat1 stat2 stat3 stat4
## 1 42.36548 1.356213 2.699796 2.3801832 0.1883335 -1.2284011 -0.5999233
## 2 26.63295 4.053961 2.375127 -1.4069480 1.8140973 1.6204884 2.6422672
## 3 20.09693 3.079888 4.488420 -0.7672566 -0.1230289 1.1415752 2.9805934
## 4 32.60415 1.355396 3.402398 0.4371202 -1.9355906 0.9028624 -1.6025400
## 5 44.58361 1.940301 2.249011 2.4492466 -0.6172000 -2.5520642 -2.1485929
## 6 28.75102 2.500499 5.563972 -1.7899084 1.8853619 2.4154840 -2.6022179
## stat5 stat6 stat7 stat8 stat9 stat10
## 1 0.148893163 -0.6622978 -2.4851868 0.3647782 2.5364335 2.92067981
## 2 1.920768980 1.7411555 -1.9599979 -2.0190558 -1.3732762 -0.31642506
## 3 2.422584300 -0.4166040 2.2205689 -2.6741531 0.4844292 2.73379230
## 4 -0.001795933 -0.6946563 -0.3693534 -0.9709467 1.7960306 0.74771154
## 5 -2.311132430 -1.0166832 2.7269876 1.5424492 -1.3156369 -0.09767897
## 6 -1.785491470 -1.8599915 1.4875095 2.0188572 -1.4892503 -1.41103566
## stat11 stat12 stat13 stat14 stat15 stat16
## 1 -2.3228905 -2.480567 -0.6335157 -0.3650149 -0.5322812 0.6029300
## 2 -0.8547903 1.119316 0.7227427 0.2121097 -0.1452281 -2.0361528
## 3 -2.1821580 2.865401 -2.9756081 2.9871745 1.9539525 -1.8857163
## 4 1.3982378 1.856765 -1.0379983 2.3341896 2.3057184 -2.8947697
## 5 0.9567220 2.567549 0.3184886 1.0307668 0.1644241 -0.6613821
## 6 0.5341771 -1.461822 0.4402476 -1.9282095 -0.3680157 1.8188807
## stat17 stat18 stat19 stat20 stat21 stat22
## 1 -1.04516208 2.3544915 2.4049001 0.2633883 -0.9788178 1.7868229
## 2 0.09513074 0.4727738 1.8899702 2.7892542 -1.3919091 -1.7198164
## 3 0.40285346 1.4655282 -1.4952788 2.9162340 -2.3893208 2.8161423
## 4 2.97446084 2.3896182 2.3083484 -1.1894441 -2.1982553 1.3666242
## 5 -0.98465055 0.6900643 1.5894209 -2.1204538 1.7961155 -0.9362189
## 6 -1.45726359 -2.1139548 -0.3964904 1.1764175 -2.9100556 -2.1359294
## stat23 stat24 stat25 stat26 stat27 stat28
## 1 -2.3718851 2.8580718 -0.4719713 -2.817086 -0.9518474 2.88892484
## 2 -2.3293245 1.5577759 -1.9569720 1.554194 -0.5081459 -1.58715141
## 3 -2.5402296 0.1422861 0.3572798 -1.051886 -2.1541717 0.03074004
## 4 -1.9679050 -1.4077642 2.5097435 1.683121 -0.2549745 -2.90384054
## 5 2.0523429 -2.2084844 -1.9280857 -2.116736 1.8180779 -1.42167580
## 6 0.2184991 -0.7599817 2.6880329 -2.903350 -1.0733233 -2.92416644
## stat29 stat30 stat31 stat32 stat33 stat34
## 1 0.7991088 -2.0059092 -0.2461502 0.6482101 -2.87462163 -0.3601543
## 2 1.9758110 -0.3874187 1.3566630 2.6493473 2.28463054 1.8591728
## 3 -0.4460218 1.0279679 1.3998452 -1.0183365 1.41109037 -2.4183984
## 4 1.0571996 2.5588036 -2.9830337 -1.1299983 0.05470414 -1.5566561
## 5 0.8854889 2.2774174 2.6499031 2.3053405 -2.39148426 -1.8272992
## 6 -0.8405267 0.1311945 0.4321289 -2.9622040 -2.55387473 2.6396458
## stat35 stat36 stat37 stat38 stat39 stat40
## 1 2.4286051 -0.5420244 -2.6782637 -2.8874269 -0.8945006 1.1749642
## 2 1.3709245 -1.3714181 1.3901204 1.2273489 -0.8934880 1.0540369
## 3 -0.9805572 2.0571353 0.8845031 2.0574493 1.1222047 1.8528618
## 4 1.0969149 -2.2820673 1.8852408 0.5391517 2.7334342 -0.4372566
## 5 -1.0971669 1.4867796 -2.3738465 -0.3743561 1.4266498 1.2551680
## 6 0.4584349 -2.2696617 -0.9935142 -0.5350499 -0.7874799 2.0009417
## stat41 stat42 stat43 stat44 stat45 stat46
## 1 -1.0474428 -1.3909023 2.54110503 -1.4320793 0.6298335 -2.09296608
## 2 2.5380247 1.6476108 0.44128850 -2.5049477 1.2726039 1.72492969
## 3 1.1477574 0.2288794 0.08891252 2.3044751 -0.7735722 -0.07302936
## 4 -1.3808300 -2.7900956 2.38297582 0.1686397 -2.1591296 1.60828602
## 5 0.2257536 1.9542116 2.66429019 0.8026123 -1.5521187 1.61751962
## 6 -1.3364114 -2.2898803 2.80735397 -0.8413086 1.0057797 -1.50653386
## stat47 stat48 stat49 stat50 stat51 stat52
## 1 -2.8318939 2.1445766 0.5668035 0.1544579 0.6291955 2.2197027
## 2 -0.5804687 -1.3689737 1.4908396 1.2465997 0.8896304 -2.6024318
## 3 0.7918019 1.5712964 1.1038082 -0.2545658 -2.1662638 0.2660159
## 4 -1.8894132 0.5680230 -0.7023218 -0.3972188 0.1578027 2.1770194
## 5 2.1088455 -2.7195437 2.1961412 -0.2615084 1.2109556 0.8260623
## 6 -1.4400891 -0.9421459 -1.7324599 -2.1720727 -2.8129435 0.6958785
## stat53 stat54 stat55 stat56 stat57 stat58
## 1 2.176805 0.5546907 -2.19704103 -0.2884173 1.3232913 -1.32824039
## 2 -2.107441 1.3864788 0.08781975 1.9998228 0.8014438 -0.26979154
## 3 1.234197 2.1337581 1.65231645 -0.4388691 -0.1811156 2.11277962
## 4 2.535406 -2.1387620 0.12856023 -1.9906180 0.9626449 1.65232646
## 5 -2.457080 2.1633499 0.60441124 2.5449364 -1.4978440 2.60542655
## 6 2.003033 -0.5379940 -2.19647264 -1.1954677 -0.5974466 -0.04703835
## stat59 stat60 stat61 stat62 stat63 stat64
## 1 1.24239659 -2.5798278 1.327928 1.68560362 0.6284891 -1.6798652
## 2 0.06379301 0.9465770 1.116928 0.03128772 -2.1944375 0.3382609
## 3 0.93223447 2.4597080 0.465251 -1.71033382 -0.5156728 1.8276784
## 4 -0.29840910 0.7273473 -2.313066 -1.47696018 2.5910559 -1.5127999
## 5 -1.17610002 -1.7948418 -2.669305 0.17813617 2.8956099 2.9411416
## 6 -1.01793981 0.2817057 2.228023 -0.86494124 -0.9747949 -0.1569053
## stat65 stat66 stat67 stat68 stat69 stat70
## 1 -2.9490898 -0.3325469 1.5745990 -2.2978280 1.5451891 -1.345990
## 2 -1.1174885 -1.5728682 -2.9229002 0.2658547 -1.9616533 2.506130
## 3 -0.2231264 -0.4503301 0.7932286 -1.2453773 -2.2309763 2.309761
## 4 -0.3522418 -2.0720532 0.9442933 2.9212906 0.5100371 -2.441108
## 5 -2.1648991 1.2002029 2.8266985 0.7461294 1.6772674 -1.280000
## 6 -2.2295458 1.1446493 0.2024925 -0.2983998 -2.8203752 1.224030
## stat71 stat72 stat73 stat74 stat75 stat76
## 1 1.0260956 2.1071210 2.6625669 -2.8924677 -0.02132523 -2.5058765
## 2 0.3525076 1.6922342 -1.2167022 -1.7271879 2.21176434 1.9329683
## 3 -2.1799035 -2.2645276 0.1415582 0.9887453 1.95592320 0.2951785
## 4 -2.4051409 2.0876484 -0.8632146 0.4011389 -1.16986716 -1.2391174
## 5 1.3538754 -0.8089395 -0.5122626 -2.1696892 1.07344925 2.6696169
## 6 -2.8073371 -1.4450488 0.5481212 -1.4381690 0.80917043 -0.1365944
## stat77 stat78 stat79 stat80 stat81 stat82
## 1 -2.5631845 -2.40331340 0.38416120 -1.2564875 -0.1550840 -1.1762617
## 2 -0.4462085 0.38400793 1.80483031 -0.8387642 0.7624431 0.9936900
## 3 1.6757870 -1.81900752 2.70904708 -0.3201959 2.5754235 1.6346260
## 4 -2.1012006 -2.24691081 1.78056848 1.0323739 1.0762523 2.1343851
## 5 -2.5736733 -1.99958372 -0.05388495 -2.5630073 -2.8783002 -0.5752426
## 6 1.6143972 0.03233746 2.90835762 1.4000487 2.9275615 -2.8503830
## stat83 stat84 stat85 stat86 stat87 stat88
## 1 1.2840565 -2.6794965 1.3956039 -1.5290235 2.221152 2.3794982
## 2 -0.2380048 1.9314318 -1.6747955 -0.3663656 1.582659 -0.5222489
## 3 -0.9150769 -1.5520337 2.4186287 2.7273662 1.306642 0.1320062
## 4 -2.5824408 -2.7775943 0.5085060 0.4689015 2.053348 0.7957955
## 5 -1.0017741 -0.2009138 0.3770109 2.4335201 -1.118058 1.3953410
## 6 2.4891765 2.9931953 -1.4171852 0.3905659 -1.856119 -2.1690490
## stat89 stat90 stat91 stat92 stat93 stat94
## 1 -0.9885110 -0.8873261 -2.7810929 -1.53325891 2.6002395 1.8890998
## 2 0.9982028 -1.2382015 -0.1574496 0.41086048 -0.5412626 -0.2421387
## 3 0.5956759 1.6871066 2.2452753 2.74279594 -1.5860478 2.9393122
## 4 2.0902634 2.1752586 -2.0677712 -2.37861037 1.1653302 0.1500632
## 5 2.9820614 0.8111660 -0.7842287 0.03766387 -1.1681970 2.1217251
## 6 -1.7428021 0.1579032 1.7456742 -0.36858466 -0.1304616 -1.4555819
## stat95 stat96 stat97 stat98 stat99 stat100
## 1 -2.6056035 -0.5814857 2.57652426 -2.3297751 2.6324007 1.445827
## 2 -2.0271583 -0.9126074 2.49582648 0.9745382 1.1339203 -2.549544
## 3 0.3823181 -0.6324139 2.46221566 1.1151560 0.4624891 0.107072
## 4 2.6414623 -0.6630505 2.10394859 1.2627635 0.4861740 1.697012
## 5 1.4642254 2.6485956 -0.07699547 0.6219473 -1.8815142 -2.685463
## 6 1.8937331 -0.4690555 1.04671776 -0.5879866 -0.9766789 2.405940
## stat101 stat102 stat103 stat104 stat105 stat106
## 1 -2.1158021 2.603936 1.7745128 -1.8903574 -1.8558655 1.0122044
## 2 -2.7998588 -2.267895 0.5336456 -0.2859477 -0.5196246 -0.9417582
## 3 0.7969509 -1.744906 -0.7960327 1.9767258 -0.2007264 -0.7872376
## 4 1.7071959 -1.540221 1.6770362 1.5395796 -0.4855365 -1.2894115
## 5 -1.4627420 -1.700983 2.4376490 0.2731541 1.5275587 1.3256483
## 6 2.6888530 1.090155 2.0769854 1.9615480 1.8689761 2.8975825
## stat107 stat108 stat109 stat110 stat111 stat112
## 1 1.954508 -0.3376471 2.503084 0.3099165 2.7209847 -2.3911204
## 2 -2.515160 0.3998704 -1.077093 2.4228268 -0.7759693 0.2513882
## 3 1.888827 1.5819857 -2.066659 -2.0008364 0.6997684 2.6157095
## 4 1.076395 -1.8524148 -2.689204 1.0985872 1.2389493 2.1018629
## 5 2.828866 -1.8590252 -2.424163 1.4391942 -0.6173239 -1.5218846
## 6 -1.419639 0.7888914 1.996463 0.9813507 0.9034198 1.3810679
## stat113 stat114 stat115 stat116 stat117 stat118
## 1 -1.616161 1.0878664 0.9860094 -0.06288462 -1.013501 -1.2212842
## 2 -1.554771 1.8683100 0.4880588 -0.63865489 -1.610217 -1.7713343
## 3 -2.679801 -2.9486952 1.7753417 0.90311784 -1.318836 -0.1429040
## 4 2.459229 -0.5584171 0.4419581 -0.09586351 0.595442 0.2883342
## 5 -2.102200 1.6300170 -2.3498287 1.36771894 -1.912202 -0.2563821
## 6 -1.835037 0.6577786 -2.9928374 2.13540316 -1.437299 -0.9570006
## stat119 stat120 stat121 stat122 stat123 stat124
## 1 2.9222729 1.9151262 1.6686068 2.0061224 1.5723072 0.78819227
## 2 2.1828208 0.8283178 -2.4458632 1.7133740 1.1393738 -0.07182054
## 3 0.9721319 1.2723130 2.8002086 2.7670381 -2.2252586 2.17499113
## 4 -1.9327896 -2.5369370 1.7835028 1.0262097 -1.8790983 -0.43639564
## 5 1.3230809 -2.8145256 -0.9547533 -2.0435417 -0.2758764 -1.85668027
## 6 0.1720700 -1.4568460 1.4115051 -0.9878145 2.3895061 -2.33730745
## stat125 stat126 stat127 stat128 stat129 stat130
## 1 1.588372 1.1620011 -0.2474264 1.650328 2.5147598 0.37283245
## 2 -1.173771 0.8162020 0.3510315 -1.263667 1.7245284 -0.72852904
## 3 -1.503497 -0.5656394 2.8040256 -2.139287 -1.7221642 2.17899609
## 4 1.040967 -2.9039600 0.3103742 1.462339 -1.2940350 -2.95015502
## 5 -2.866184 1.6885070 -2.2525666 -2.628631 1.8581577 2.80127025
## 6 -1.355111 1.5017927 0.4295921 -0.580415 0.9851009 -0.03773117
## stat131 stat132 stat133 stat134 stat135 stat136
## 1 -0.09028241 0.5194538 2.8478346 2.6664724 -2.0206311 1.398415090
## 2 -0.53045595 1.4134049 2.9180586 0.3299096 1.4784122 -1.278896090
## 3 1.35843194 0.2279946 0.3532595 0.6138676 -0.3443284 0.057763811
## 4 -1.92450273 1.2698178 -1.5299660 -2.6083462 1.1665530 -0.187791914
## 5 1.49036849 2.6337729 -2.3206244 0.4978287 -1.7397571 0.001200184
## 6 -0.64642709 -1.9256228 1.7032650 -0.9152725 -0.3188055 2.155395980
## stat137 stat138 stat139 stat140 stat141 stat142
## 1 -1.2794871 0.4064890 -0.4539998 2.6660173 -1.8375313 0.4711883
## 2 -2.7709017 -1.6303773 -1.9025910 0.2572918 0.6612002 1.4764348
## 3 -1.1930757 -0.1051243 -0.5108380 -1.0879666 2.4969513 -0.9477230
## 4 -1.2318919 2.2348571 0.1788580 -1.5851788 -1.2384283 -2.1859181
## 5 1.8685058 2.7229517 -2.9077182 2.6606939 -1.5963592 -2.2213492
## 6 -0.4807318 -1.2117369 -0.9358531 -2.5100758 -2.3803916 -0.7096854
## stat143 stat144 stat145 stat146 stat147 stat148
## 1 1.9466263 2.2689433 -0.3597288 -0.6551386 1.65438592 0.6404466
## 2 1.3156421 2.4459090 -0.3790028 1.4858465 -0.07784461 1.0096149
## 3 0.1959563 2.3062942 1.8459278 2.6848175 -2.70935774 -1.2093409
## 4 1.7633296 -2.8171508 2.0902622 -2.6625464 -1.12600601 -2.1926479
## 5 0.3885758 1.8160636 2.8257299 -1.4526173 1.60679603 2.3807991
## 6 0.7623450 0.2692145 -2.4307463 -2.1244523 -2.67803812 -1.5273387
## stat149 stat150 stat151 stat152 stat153 stat154
## 1 0.1583575 0.4755351 0.3213410 2.0241520 1.5720103 -0.1825875
## 2 -0.4311406 2.9577663 0.6937252 0.1397280 0.3775735 -1.1012636
## 3 -0.8352824 2.5716205 1.7528236 0.4326277 -2.2334397 -2.6265771
## 4 -2.8069143 1.8813509 2.3358023 0.1015632 1.2117474 -1.3714278
## 5 -1.6166265 1.1112266 -1.1998471 2.9316769 -2.1676455 -0.3411089
## 6 -0.2265472 2.7264354 -1.6746094 -2.3376281 -1.7022788 -1.2352397
## stat155 stat156 stat157 stat158 stat159 stat160
## 1 -1.139657 0.07061254 0.5893906 -1.9920996 -2.83714366 2.249398
## 2 -2.041093 0.74047768 2.5415072 -1.2697256 -1.64364433 -2.448922
## 3 -1.219507 -0.55198693 0.4046920 1.2098547 -0.90412390 -1.934093
## 4 2.992191 2.33222485 2.0622969 -0.6714653 2.76836085 -1.431120
## 5 -2.362356 -1.23906672 0.4746319 -0.7849202 0.69399995 2.052411
## 6 -1.604499 1.31051409 -0.5164744 0.6288667 0.07899523 -2.287402
## stat161 stat162 stat163 stat164 stat165 stat166
## 1 1.7182635 -1.2323593 2.7350423 1.0707235 1.1621544 0.9493989
## 2 -0.6247674 2.6740098 2.8211024 1.5561292 -1.1027147 1.0519739
## 3 -0.6230453 -0.7993517 -2.8318374 -1.1148673 1.4261659 0.5294309
## 4 1.7644744 0.1696584 1.2653207 0.6621516 0.9470508 0.1985014
## 5 -1.2070210 0.7243784 0.9736322 2.7426259 -2.6862383 1.6840212
## 6 2.3705316 -2.1667893 -0.2516685 -0.8425958 -1.9099342 -2.8607297
## stat167 stat168 stat169 stat170 stat171 stat172
## 1 0.1146510 2.3872008 1.1180918 -0.95370555 -2.25076509 0.2348182
## 2 1.0760417 -2.0449336 0.9715676 -0.40173489 -0.11953555 -2.3107369
## 3 1.1735898 1.3860190 -2.2894719 0.06350347 0.29191551 -1.6079744
## 4 2.5511832 0.5446648 1.2694012 -0.84571201 0.79789722 0.2623538
## 5 2.2900002 2.6289782 -0.2783571 1.39032829 -0.55532032 1.0499046
## 6 -0.7513983 2.9617066 -2.2119520 -1.71958113 -0.01452018 -0.2751517
## stat173 stat174 stat175 stat176 stat177 stat178
## 1 1.79366076 -1.920206 -0.38841942 0.8530301 1.64532077 -1.1354179
## 2 -0.07484659 1.337846 2.20911694 0.9616837 -2.80810070 -2.1136749
## 3 -1.05521810 -1.483741 0.06148359 2.3066039 -0.34688616 1.1840581
## 4 0.31460321 1.195741 2.97633862 1.1685091 -0.06346265 1.4205489
## 5 -1.39428365 2.458523 0.64836472 -1.0396386 -0.57828104 -0.5006818
## 6 2.31844401 1.239864 -2.06490874 0.7696204 -1.77586019 2.0855925
## stat179 stat180 stat181 stat182 stat183 stat184
## 1 2.0018647 0.1476815 -1.27279520 1.9181504 -0.5297624 -2.9718938
## 2 -2.1351449 2.9012582 -1.09914911 -2.5488517 -2.8377736 1.4073374
## 3 -1.7819908 2.9902627 0.81908613 0.2503852 0.3712984 -2.1714024
## 4 -0.1026974 -2.4763253 -2.52645421 1.3096315 2.1458161 -1.5228094
## 5 -2.2298794 2.4465680 -0.70346898 -1.6997617 2.9178164 -0.3615532
## 6 -1.1168108 1.5552123 -0.01361342 1.7338791 -1.1104763 0.1882416
## stat185 stat186 stat187 stat188 stat189 stat190
## 1 -0.1043832 -1.5047463 2.700351 -2.4780862 -1.9078265 0.9978108
## 2 -2.0310574 -0.5380074 -1.963275 -1.2221278 -2.4290681 -1.9515115
## 3 2.6727278 1.2688179 -1.399018 -2.9612138 2.6456394 2.0073323
## 4 -2.7796295 2.0682354 2.243727 0.4296881 0.1931333 2.2710960
## 5 -0.6231265 2.5833981 2.229041 0.8139584 1.4544131 1.8886451
## 6 2.7204690 -2.4469144 -1.421998 1.7477882 -0.1481806 0.6011560
## stat191 stat192 stat193 stat194 stat195 stat196
## 1 -0.6644351 2.6270833 -1.1094601 -2.4200392 2.870713 -0.6590932
## 2 -0.6483142 1.4519118 -0.1963493 -2.3025322 1.255608 2.1617947
## 3 -1.5457382 -0.2977442 -1.7045015 0.7962404 -1.696063 -1.4771117
## 4 -1.1780495 -2.9747574 -1.1471518 -1.2377013 -1.010672 -2.6055975
## 5 2.8813178 -1.8964081 -1.2653487 -1.7839754 -2.872581 2.3033464
## 6 0.4437973 0.6599325 -1.4029555 -2.3118258 -1.792232 1.3934380
## stat197 stat198 stat199 stat200 stat201 stat202
## 1 -0.83056986 0.9550526 -1.7025776 -2.8263099 -0.7023998 0.2272806
## 2 -1.42178249 -1.2471864 2.5723093 -0.0233496 -1.8975239 1.9472262
## 3 -0.19233958 -0.5161456 0.0279946 -1.2333704 -2.9672263 -2.8666208
## 4 -1.23145902 1.4728470 -0.4562025 -2.2983441 -1.5101184 0.2530525
## 5 1.85018563 -1.8269292 -0.6337969 -2.1473246 0.9909850 1.0950903
## 6 -0.09311061 0.5144456 -2.8178268 -2.7555969 -2.3546004 -1.0558939
## stat203 stat204 stat205 stat206 stat207 stat208
## 1 1.166631220 0.007453276 2.9961641 1.5327307 -2.2293356 -0.9946009
## 2 -0.235396504 2.132749800 0.3707606 1.5604026 -1.0089217 2.1474257
## 3 0.003180946 2.229793310 2.7354040 0.8992231 2.9694967 2.3081024
## 4 -0.474482715 -1.584772230 -2.3224132 -0.9409741 -2.3179255 0.8032548
## 5 2.349412920 -1.276320220 -2.0203719 -1.1733509 1.0371852 -2.5086207
## 6 0.727436960 -0.960191786 -0.8964998 -1.6406623 -0.2330488 1.7993879
## stat209 stat210 stat211 stat212 stat213 stat214
## 1 -2.2182105 -1.4099774 -1.656754 2.6602585 -2.9270992 1.1240714
## 2 -2.8932488 -1.1641679 -2.605423 -1.5650513 2.9523673 2.0266318
## 3 -1.8279589 0.0472350 -2.026734 2.5054367 0.9903042 0.3274105
## 4 -1.0878067 0.1171303 2.645891 -1.6775225 1.3452160 1.4694063
## 5 -0.8158175 0.4060950 0.912256 0.2925677 2.1610141 0.5679936
## 6 -2.2664354 -0.2061083 -1.435174 2.6645632 0.4216259 -0.6419122
## stat215 stat216 stat217
## 1 -2.7510750 -0.5501796 1.2638469
## 2 2.8934650 -2.4099574 -1.2411407
## 3 -1.0947676 1.2852937 1.5411530
## 4 0.6343777 0.1345372 2.9102673
## 5 0.9908702 1.7909757 -2.0902610
## 6 -2.8113887 -1.0624912 0.2765074features = features.highprec
#str(features) Checking correlations to evaluate removal of redundant features
corr.matrix = round(cor(features[sapply(features, is.numeric)]),2)
# filter out only highly correlated variables
threshold = 0.6
corr.matrix.tmp = corr.matrix
diag(corr.matrix.tmp) = 0
high.corr = apply(abs(corr.matrix.tmp) >= threshold, 1, any)
high.corr.matrix = corr.matrix.tmp[high.corr, high.corr]
DT::datatable(corr.matrix)DT::datatable(high.corr.matrix)Feature Names
feature.names = colnames(features)
drops <- c('JobName')
feature.names = feature.names[!(feature.names %in% drops)]
#str(feature.names)Read and Clean Labels
labels = read.csv("../../Data/labels.csv")
#str(labels)
labels = labels[,c("JobName", output.var)]
summary(labels)## JobName y3
## Job_00001: 1 Min. : 95.91
## Job_00002: 1 1st Qu.:118.21
## Job_00003: 1 Median :123.99
## Job_00004: 1 Mean :125.36
## Job_00005: 1 3rd Qu.:131.06
## Job_00006: 1 Max. :193.73
## (Other) :9994 NA's :2497Merge Datasets
data <- merge(features, labels, by = 'JobName')
drops <- c('JobName')
data = data[,(!colnames(data) %in% drops)]
#str(data)Transformations
if (transform.abs == TRUE){
data[,label.names] = 10^(data[,label.names]/20)
data = filter(data, y3 < 1E7)
}
#str(data)
if (log.pred == TRUE){
data[label.names] = log(data[alt.scale.label.name],10)
drops = c(alt.scale.label.name)
data = data[!(names(data) %in% drops)]
}
#str(data)Remove NA Cases
data = data[complete.cases(data),]Exploratory Data Analysis
Check correlation of Label with Featires
if (eda == TRUE){
corr.to.label =round(cor(dplyr::select(data,-one_of(label.names)),dplyr::select_at(data,label.names)),4)
DT::datatable(corr.to.label)
}Multicollinearity - VIF
if (eda == TRUE){
vifDF = usdm::vif(select_at(data,feature.names)) %>% arrange(desc(VIF))
head(vifDF,10)
}Scatterplots
panel.hist <- function(x, ...)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col = "cyan", ...)
}if (eda == TRUE){
histogram(data[ ,label.names])
#hist(data[complete.cases(data),alt.scale.label.name])
}# https://stackoverflow.com/questions/24648729/plot-one-numeric-variable-against-n-numeric-variables-in-n-plots
ind.pairs.plot <- function(data, xvars=NULL, yvar)
{
df <- data
if (is.null(xvars)) {
xvars = names(data[which(names(data)!=yvar)])
}
#choose a format to display charts
ncharts <- length(xvars)
for(i in 1:ncharts){
plot(df[,xvars[i]],df[,yvar], xlab = xvars[i], ylab = yvar)
}
}
if (eda == TRUE){
ind.pairs.plot(data, feature.names, label.names)
}
#
# pl <- ggplot(data, aes(x=x18, y = y3))
# pl2 <- pl + geom_point(aes(alpha = 0.1)) # default color gradient based on 'hp'
# print(pl2)Feature Engineering
if(eda ==FALSE){
# x18 may need transformations
plot(data[,'x18'], data[,label.names], main = "Original Scatter Plot vs. x18", ylab = label.names, xlab = 'x18')
plot(sqrt(data[,'x18']), data[,label.names], main = "Original Scatter Plot vs. sqrt(x18)", ylab = label.names, xlab = 'sqrt(x18)')
# transforming x18
data$sqrt.x18 = sqrt(data$x18)
data = dplyr::select(data,-one_of('x18'))
# what about x7, x9?
# x11 looks like data is at discrete points after a while. Will this be a problem?
}
Modeling
Train Test Split
data = data[sample(nrow(data)),] # randomly shuffle data
split = sample.split(data[,label.names], SplitRatio = 0.8)
data.train = subset(data, split == TRUE)
data.test = subset(data, split == FALSE)Common Functions
plot.diagnostics <- function(model, train) {
plot(model)
residuals = resid(model) # Plotted above in plot(lm.out)
r.standard = rstandard(model)
r.student = rstudent(model)
plot(predict(model,train),r.student,
ylab="Student Residuals", xlab="Predicted Values",
main="Student Residual Plot")
abline(0, 0)
plot(predict(model, train),r.standard,
ylab="Standard Residuals", xlab="Predicted Values",
main="Standard Residual Plot")
abline(0, 0)
abline(2, 0)
abline(-2, 0)
# Histogram
hist(r.student, freq=FALSE, main="Distribution of Studentized Residuals",
xlab="Studentized Residuals", ylab="Density", ylim=c(0,0.5))
# Create range of x-values for normal curve
xfit <- seq(min(r.student)-1, max(r.student)+1, length=40)
# Generate values from the normal distribution at the specified values
yfit <- (dnorm(xfit))
# Add the normal curve
lines(xfit, yfit, ylim=c(0,0.5))
# http://www.stat.columbia.edu/~martin/W2024/R7.pdf
# Influential plots
inf.meas = influence.measures(model)
# print (summary(inf.meas)) # too much data
# Leverage plot
lev = hat(model.matrix(model))
plot(lev, ylab = 'Leverage - check')
# Cook's Distance
cd = cooks.distance(model)
plot(cd,ylab="Cooks distances")
abline(4/nrow(train),0)
abline(1,0)
print (paste("Number of data points that have Cook's D > 4/n: ", length(cd[cd > 4/nrow(train)]), sep = ""))
print (paste("Number of data points that have Cook's D > 1: ", length(cd[cd > 1]), sep = ""))
return(cd)
}
train.caret.glmselect = function(formula, data, method
,subopt = NULL, feature.names
, train.control = NULL, tune.grid = NULL, pre.proc = NULL){
if(is.null(train.control)){
train.control <- trainControl(method = "cv"
,number = 10
,search = "grid"
,verboseIter = TRUE
,allowParallel = TRUE
)
}
if(is.null(tune.grid)){
if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
tune.grid = data.frame(nvmax = 1:length(feature.names))
}
if (method == 'glmnet' && subopt == 'LASSO'){
# Will only show 1 Lambda value during training, but that is OK
# https://stackoverflow.com/questions/47526544/why-need-to-tune-lambda-with-carettrain-method-glmnet-and-cv-glmnet
# Another option for LASSO is this: https://github.com/topepo/caret/blob/master/RegressionTests/Code/lasso.R
lambda = 10^seq(-2,0, length =100)
alpha = c(1)
tune.grid = expand.grid(alpha = alpha,lambda = lambda)
}
if (method == 'lars'){
# https://github.com/topepo/caret/blob/master/RegressionTests/Code/lars.R
fraction = seq(0, 1, length = 100)
tune.grid = expand.grid(fraction = fraction)
pre.proc = c("center", "scale")
}
}
# http://sshaikh.org/2015/05/06/parallelize-machine-learning-in-r-with-multi-core-cpus/
cl <- makeCluster(detectCores()*0.75) # use 75% of cores only, leave rest for other tasks
registerDoParallel(cl)
set.seed(1)
# note that the seed has to actually be set just before this function is called
# settign is above just not ensure reproducibility for some reason
model.caret <- caret::train(formula
, data = data
, method = method
, tuneGrid = tune.grid
, trControl = train.control
, preProc = pre.proc
)
stopCluster(cl)
registerDoSEQ() # register sequential engine in case you are not using this function anymore
if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
print(model.caret$results) # all model results
print(model.caret$bestTune) # best model
model = model.caret$finalModel
# Metrics Plot
dataPlot = model.caret$results %>%
gather(key='metric',value='value',-nvmax) %>%
dplyr::filter(metric %in% c('MAE','RMSE','Rsquared'))
metricsPlot = ggplot(data=dataPlot,aes(x=nvmax,y=value) ) +
geom_line(color='lightblue4') +
geom_point(color='blue',alpha=0.7,size=.9) +
facet_wrap(~metric,ncol=4,scales='free_y')+
theme_light()
plot(metricsPlot)
# Residuals Plot
# leap function does not support studentized residuals
dataPlot=data.frame(pred=predict(model.caret,data),res=resid(model.caret))
residPlot = ggplot(dataPlot,aes(x=pred,y=res)) +
geom_point(color='light blue',alpha=0.7) +
geom_smooth()+
theme_light()
plot(residPlot)
residHistogram = ggplot(dataPlot,aes(x=res)) +
geom_histogram(aes(y=..density..),fill='light blue',alpha=1) +
geom_density(color='lightblue4') +
theme_light()
plot(residHistogram)
# Provides the coefficients of the best model
id = rownames(model.caret$bestTune)
message("Coefficients of final model:")
print (coef(model, id = id))
return(list(model = model,id = id, residPlot = residPlot, residHistogram=residHistogram))
}
if (method == 'glmnet' && subopt == 'LASSO'){
print(model.caret)
print(plot(model.caret))
print(model.caret$bestTune)
print(model.caret$results)
# Metrics Plot
dataPlot = model.caret$results %>%
gather(key='metric',value='value',-lambda) %>%
dplyr::filter(metric %in% c('MAE','RMSE','Rsquared'))
metricsPlot = ggplot(data=dataPlot,aes(x=lambda,y=value) ) +
geom_line(color='lightblue4') +
geom_point(color='blue',alpha=0.7,size=.9) +
facet_wrap(~metric,ncol=4,scales='free_y')+
theme_light()
plot(metricsPlot)
# Residuals Plot
dataPlot=data.frame(pred=predict(model.caret,data),res=resid(model.caret))
residPlot = ggplot(dataPlot,aes(x=pred,y=res)) +
geom_point(color='light blue',alpha=0.7) +
geom_smooth()+
theme_light()
plot(residPlot)
residHistogram = ggplot(dataPlot,aes(x=res)) +
geom_histogram(aes(y=..density..),fill='light blue',alpha=1) +
geom_density(color='lightblue4') +
theme_light()
plot(residHistogram)
id = NULL # not really needed but added for consistency
return(list(model = model.caret,id = id, residPlot = residPlot, metricsPlot=metricsPlot ))
}
if (method == 'lars'){
print(model.caret)
print(plot(model.caret))
print(model.caret$bestTune)
# Metrics Plot
dataPlot = model.caret$results %>%
gather(key='metric',value='value',-fraction) %>%
dplyr::filter(metric %in% c('MAE','RMSE','Rsquared'))
metricsPlot = ggplot(data=dataPlot,aes(x=fraction,y=value) ) +
geom_line(color='lightblue4') +
geom_point(color='blue',alpha=0.7,size=.9) +
facet_wrap(~metric,ncol=4,scales='free_y')+
theme_light()
plot(metricsPlot)
# Residuals Plot
dataPlot=data.frame(pred=predict(model.caret,data),res=resid(model.caret))
residPlot = ggplot(dataPlot,aes(x=pred,y=res)) +
geom_point(color='light blue',alpha=0.7) +
geom_smooth()+
theme_light()
plot(residPlot)
residHistogram = ggplot(dataPlot,aes(x=res)) +
geom_histogram(aes(y=..density..),fill='light blue',alpha=1) +
geom_density(color='lightblue4') +
theme_light()
plot(residHistogram)
id = NULL # not really needed but added for consistency
return(list(model = model.caret,id = id, residPlot = residPlot, residHistogram=residHistogram))
}
}
# https://stackoverflow.com/questions/48265743/linear-model-subset-selection-goodness-of-fit-with-k-fold-cross-validation
# changed slightly since call[[2]] was just returning "formula" without actually returnign the value in formula
predict.regsubsets <- function(object, newdata, id, formula, ...) {
#form <- as.formula(object$call[[2]])
mat <- model.matrix(formula, newdata) # adds intercept and expands any interaction terms
coefi <- coef(object, id = id)
xvars <- names(coefi)
return(mat[,xvars]%*%coefi)
}
test.model = function(model, test, level=0.95
,draw.limits = FALSE, good = 0.1, ok = 0.15
,method = NULL, subopt = NULL
,id = NULL, formula, feature.names, label.names){
## if using caret for glm select equivalent functionality,
## need to pass formula (full is ok as it will select subset of variables from there)
if (is.null(method)){
pred = predict(model, newdata=test, interval="confidence", level = level)
}
if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
pred = predict.regsubsets(model, newdata = test, id = id, formula = formula)
}
if (method == 'glmnet' && subopt == 'LASSO'){
xtest = as.matrix(test[,feature.names])
pred=as.data.frame(predict(model, xtest))
}
if (method == 'lars'){
pred=as.data.frame(predict(model, newdata = test))
}
# Summary of predicted values
print ("Summary of predicted values: ")
print(summary(pred[,1]))
test.mse = mean((test[,label.names]-pred[,1])^2)
print (paste(method, subopt, "Test MSE:", test.mse, sep=" "))
plot(test[,label.names],pred[,1],xlab = "Actual", ylab = "Predicted")
abline(0,(1+good),col='green', lwd = 3)
abline(0,(1-good),col='green', lwd = 3)
abline(0,(1+ok),col='blue', lwd = 3)
abline(0,(1-ok),col='blue', lwd = 3)
}Setup Formulae
n <- names(data.train)
formula <- as.formula(paste(paste(n[n %in% label.names], collapse = " + ")," ~", paste(n[!n %in% label.names], collapse = " + ")))
# ind.interact = c("x4","x7","x8", "x9", "x10", "x11", "x14", "x16", "x17", "x21", "sqrt.x18")
# ind.nointeract = c("stat13", "stat14", "stat24", "stat60", "stat98", "stat110", "stat144", "stat149")
#
# interact = paste(ind.interact, collapse = " + ")
# nointeract = paste(ind.nointeract, collapse = " + ")
#
# # ^2 is 2 way interaction, ^3 is 3 way interaction
# formula <- as.formula(paste(paste(n[n %in% label.names], collapse = " + "), "~ (", interact, " )^2 ", " + ", nointeract ))
#
# # # * is all way interaction
# # formula <- as.formula(paste(paste(n[n %in% label.names], collapse = " + "), "~ (", interact, " ) ", " + ", nointeract ))
grand.mean.formula = as.formula(paste(paste(n[n %in% label.names], collapse = " + ")," ~ 1"))
print(formula)## y3 ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 +
## x12 + x13 + x14 + x15 + x16 + x17 + x19 + x20 + x21 + x22 +
## x23 + stat1 + stat2 + stat3 + stat4 + stat5 + stat6 + stat7 +
## stat8 + stat9 + stat10 + stat11 + stat12 + stat13 + stat14 +
## stat15 + stat16 + stat17 + stat18 + stat19 + stat20 + stat21 +
## stat22 + stat23 + stat24 + stat25 + stat26 + stat27 + stat28 +
## stat29 + stat30 + stat31 + stat32 + stat33 + stat34 + stat35 +
## stat36 + stat37 + stat38 + stat39 + stat40 + stat41 + stat42 +
## stat43 + stat44 + stat45 + stat46 + stat47 + stat48 + stat49 +
## stat50 + stat51 + stat52 + stat53 + stat54 + stat55 + stat56 +
## stat57 + stat58 + stat59 + stat60 + stat61 + stat62 + stat63 +
## stat64 + stat65 + stat66 + stat67 + stat68 + stat69 + stat70 +
## stat71 + stat72 + stat73 + stat74 + stat75 + stat76 + stat77 +
## stat78 + stat79 + stat80 + stat81 + stat82 + stat83 + stat84 +
## stat85 + stat86 + stat87 + stat88 + stat89 + stat90 + stat91 +
## stat92 + stat93 + stat94 + stat95 + stat96 + stat97 + stat98 +
## stat99 + stat100 + stat101 + stat102 + stat103 + stat104 +
## stat105 + stat106 + stat107 + stat108 + stat109 + stat110 +
## stat111 + stat112 + stat113 + stat114 + stat115 + stat116 +
## stat117 + stat118 + stat119 + stat120 + stat121 + stat122 +
## stat123 + stat124 + stat125 + stat126 + stat127 + stat128 +
## stat129 + stat130 + stat131 + stat132 + stat133 + stat134 +
## stat135 + stat136 + stat137 + stat138 + stat139 + stat140 +
## stat141 + stat142 + stat143 + stat144 + stat145 + stat146 +
## stat147 + stat148 + stat149 + stat150 + stat151 + stat152 +
## stat153 + stat154 + stat155 + stat156 + stat157 + stat158 +
## stat159 + stat160 + stat161 + stat162 + stat163 + stat164 +
## stat165 + stat166 + stat167 + stat168 + stat169 + stat170 +
## stat171 + stat172 + stat173 + stat174 + stat175 + stat176 +
## stat177 + stat178 + stat179 + stat180 + stat181 + stat182 +
## stat183 + stat184 + stat185 + stat186 + stat187 + stat188 +
## stat189 + stat190 + stat191 + stat192 + stat193 + stat194 +
## stat195 + stat196 + stat197 + stat198 + stat199 + stat200 +
## stat201 + stat202 + stat203 + stat204 + stat205 + stat206 +
## stat207 + stat208 + stat209 + stat210 + stat211 + stat212 +
## stat213 + stat214 + stat215 + stat216 + stat217 + sqrt.x18print(grand.mean.formula)## y3 ~ 1# Update feature.names because we may have transformed some features
feature.names = n[!n %in% label.names]Full Model
model.full = lm(formula , data.train)
summary(model.full)##
## Call:
## lm(formula = formula, data = data.train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.080 -6.100 -1.750 4.428 54.524
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.075e+01 2.769e+00 32.778 < 2e-16 ***
## x1 -1.992e-01 1.900e-01 -1.048 0.2947
## x2 1.158e-01 1.216e-01 0.952 0.3410
## x3 2.098e-02 3.333e-02 0.629 0.5290
## x4 -1.431e-02 2.632e-03 -5.435 5.72e-08 ***
## x5 4.870e-02 8.624e-02 0.565 0.5723
## x6 4.663e-02 1.742e-01 0.268 0.7889
## x7 3.339e+00 1.843e-01 18.116 < 2e-16 ***
## x8 1.109e-01 4.306e-02 2.575 0.0101 *
## x9 9.361e-01 9.630e-02 9.721 < 2e-16 ***
## x10 3.927e-01 8.945e-02 4.390 1.16e-05 ***
## x11 3.639e+07 2.146e+07 1.696 0.0900 .
## x12 -2.819e-02 5.476e-02 -0.515 0.6067
## x13 2.245e-02 2.187e-02 1.026 0.3047
## x14 -1.627e-01 9.436e-02 -1.724 0.0848 .
## x15 3.431e-02 9.012e-02 0.381 0.7035
## x16 2.873e-01 6.194e-02 4.638 3.59e-06 ***
## x17 4.325e-01 9.437e-02 4.583 4.68e-06 ***
## x19 3.053e-02 4.828e-02 0.632 0.5272
## x20 -2.197e-01 3.338e-01 -0.658 0.5104
## x21 2.897e-02 1.232e-02 2.351 0.0187 *
## x22 -7.918e-02 1.008e-01 -0.785 0.4323
## x23 -4.955e-02 9.596e-02 -0.516 0.6056
## stat1 -5.220e-02 7.245e-02 -0.721 0.4712
## stat2 4.942e-02 7.200e-02 0.686 0.4925
## stat3 1.351e-01 7.242e-02 1.866 0.0621 .
## stat4 -1.579e-01 7.222e-02 -2.187 0.0288 *
## stat5 -5.071e-02 7.283e-02 -0.696 0.4863
## stat6 -1.188e-01 7.239e-02 -1.641 0.1009
## stat7 -3.215e-02 7.221e-02 -0.445 0.6561
## stat8 -1.742e-02 7.225e-02 -0.241 0.8095
## stat9 -7.690e-03 7.223e-02 -0.106 0.9152
## stat10 -1.285e-01 7.240e-02 -1.774 0.0761 .
## stat11 -3.674e-02 7.315e-02 -0.502 0.6155
## stat12 4.804e-02 7.161e-02 0.671 0.5023
## stat13 -9.702e-02 7.217e-02 -1.344 0.1789
## stat14 -2.259e-01 7.196e-02 -3.140 0.0017 **
## stat15 -6.247e-02 7.172e-02 -0.871 0.3838
## stat16 1.167e-02 7.197e-02 0.162 0.8712
## stat17 1.223e-02 7.173e-02 0.171 0.8646
## stat18 -9.609e-02 7.194e-02 -1.336 0.1817
## stat19 1.122e-01 7.163e-02 1.566 0.1175
## stat20 -8.680e-02 7.209e-02 -1.204 0.2287
## stat21 -4.262e-02 7.270e-02 -0.586 0.5577
## stat22 -1.024e-01 7.227e-02 -1.417 0.1565
## stat23 1.731e-01 7.214e-02 2.399 0.0165 *
## stat24 -1.565e-01 7.240e-02 -2.161 0.0307 *
## stat25 -9.563e-02 7.232e-02 -1.322 0.1861
## stat26 -5.774e-02 7.176e-02 -0.805 0.4211
## stat27 -8.833e-03 7.248e-02 -0.122 0.9030
## stat28 5.402e-02 7.239e-02 0.746 0.4555
## stat29 3.798e-02 7.253e-02 0.524 0.6005
## stat30 6.557e-02 7.299e-02 0.898 0.3691
## stat31 -2.819e-02 7.280e-02 -0.387 0.6986
## stat32 2.399e-02 7.289e-02 0.329 0.7421
## stat33 -1.769e-01 7.212e-02 -2.453 0.0142 *
## stat34 1.821e-02 7.235e-02 0.252 0.8012
## stat35 -1.109e-01 7.237e-02 -1.532 0.1255
## stat36 1.326e-02 7.156e-02 0.185 0.8530
## stat37 -1.366e-01 7.289e-02 -1.875 0.0609 .
## stat38 1.693e-01 7.253e-02 2.335 0.0196 *
## stat39 -1.269e-01 7.206e-02 -1.761 0.0783 .
## stat40 2.005e-02 7.202e-02 0.278 0.7808
## stat41 -1.131e-01 7.147e-02 -1.583 0.1136
## stat42 -1.056e-01 7.207e-02 -1.466 0.1428
## stat43 -9.940e-02 7.230e-02 -1.375 0.1693
## stat44 -1.030e-02 7.189e-02 -0.143 0.8861
## stat45 -1.059e-01 7.241e-02 -1.463 0.1435
## stat46 7.012e-02 7.292e-02 0.962 0.3363
## stat47 9.436e-02 7.274e-02 1.297 0.1946
## stat48 6.637e-02 7.221e-02 0.919 0.3581
## stat49 3.700e-02 7.176e-02 0.516 0.6062
## stat50 8.195e-02 7.186e-02 1.140 0.2542
## stat51 1.034e-01 7.198e-02 1.436 0.1510
## stat52 -7.706e-02 7.222e-02 -1.067 0.2860
## stat53 -8.366e-03 7.291e-02 -0.115 0.9086
## stat54 -9.544e-02 7.257e-02 -1.315 0.1885
## stat55 7.299e-02 7.166e-02 1.018 0.3085
## stat56 -1.112e-02 7.252e-02 -0.153 0.8781
## stat57 -5.677e-02 7.184e-02 -0.790 0.4294
## stat58 5.987e-03 7.155e-02 0.084 0.9333
## stat59 6.685e-02 7.202e-02 0.928 0.3534
## stat60 1.186e-01 7.233e-02 1.639 0.1012
## stat61 1.279e-02 7.204e-02 0.178 0.8591
## stat62 -3.736e-02 7.186e-02 -0.520 0.6032
## stat63 8.660e-02 7.234e-02 1.197 0.2313
## stat64 -9.877e-02 7.155e-02 -1.380 0.1675
## stat65 -7.995e-02 7.258e-02 -1.102 0.2707
## stat66 9.041e-02 7.330e-02 1.233 0.2175
## stat67 -1.127e-02 7.275e-02 -0.155 0.8769
## stat68 -4.156e-02 7.239e-02 -0.574 0.5660
## stat69 -2.717e-03 7.187e-02 -0.038 0.9698
## stat70 6.679e-02 7.192e-02 0.929 0.3531
## stat71 -1.903e-02 7.192e-02 -0.265 0.7913
## stat72 6.953e-02 7.275e-02 0.956 0.3393
## stat73 1.179e-01 7.243e-02 1.628 0.1035
## stat74 -2.409e-02 7.246e-02 -0.332 0.7395
## stat75 -4.956e-02 7.280e-02 -0.681 0.4961
## stat76 -4.243e-03 7.231e-02 -0.059 0.9532
## stat77 -5.256e-02 7.219e-02 -0.728 0.4666
## stat78 -6.536e-02 7.248e-02 -0.902 0.3673
## stat79 -5.659e-03 7.226e-02 -0.078 0.9376
## stat80 3.688e-02 7.244e-02 0.509 0.6107
## stat81 7.130e-02 7.245e-02 0.984 0.3251
## stat82 8.624e-02 7.235e-02 1.192 0.2333
## stat83 -1.186e-02 7.223e-02 -0.164 0.8696
## stat84 1.048e-03 7.237e-02 0.014 0.9884
## stat85 -8.723e-03 7.237e-02 -0.121 0.9041
## stat86 -1.255e-02 7.255e-02 -0.173 0.8626
## stat87 -7.880e-02 7.247e-02 -1.087 0.2769
## stat88 -7.227e-02 7.209e-02 -1.002 0.3161
## stat89 -9.006e-02 7.192e-02 -1.252 0.2106
## stat90 -4.511e-02 7.238e-02 -0.623 0.5332
## stat91 -4.485e-02 7.181e-02 -0.625 0.5323
## stat92 -1.365e-01 7.208e-02 -1.893 0.0584 .
## stat93 -1.161e-01 7.308e-02 -1.588 0.1122
## stat94 -5.654e-02 7.267e-02 -0.778 0.4366
## stat95 1.831e-02 7.204e-02 0.254 0.7994
## stat96 -2.878e-02 7.206e-02 -0.399 0.6896
## stat97 -8.116e-05 7.184e-02 -0.001 0.9991
## stat98 1.027e+00 7.138e-02 14.394 < 2e-16 ***
## stat99 4.184e-02 7.244e-02 0.578 0.5635
## stat100 1.863e-01 7.243e-02 2.572 0.0101 *
## stat101 -7.866e-02 7.302e-02 -1.077 0.2814
## stat102 6.142e-03 7.254e-02 0.085 0.9325
## stat103 -6.664e-02 7.303e-02 -0.912 0.3616
## stat104 -7.611e-02 7.195e-02 -1.058 0.2902
## stat105 8.686e-02 7.170e-02 1.212 0.2257
## stat106 -1.052e-01 7.203e-02 -1.461 0.1442
## stat107 -1.006e-01 7.232e-02 -1.392 0.1641
## stat108 -5.691e-02 7.219e-02 -0.788 0.4306
## stat109 1.218e-02 7.240e-02 0.168 0.8664
## stat110 -9.773e-01 7.196e-02 -13.581 < 2e-16 ***
## stat111 2.472e-02 7.228e-02 0.342 0.7324
## stat112 2.515e-02 7.266e-02 0.346 0.7293
## stat113 -4.837e-02 7.262e-02 -0.666 0.5054
## stat114 2.615e-02 7.268e-02 0.360 0.7190
## stat115 9.549e-02 7.227e-02 1.321 0.1865
## stat116 8.705e-02 7.300e-02 1.192 0.2332
## stat117 4.194e-02 7.252e-02 0.578 0.5631
## stat118 -7.551e-02 7.181e-02 -1.051 0.2931
## stat119 5.965e-02 7.214e-02 0.827 0.4083
## stat120 4.330e-02 7.178e-02 0.603 0.5464
## stat121 -6.854e-02 7.266e-02 -0.943 0.3456
## stat122 -4.274e-02 7.199e-02 -0.594 0.5527
## stat123 3.010e-02 7.302e-02 0.412 0.6802
## stat124 -3.671e-02 7.194e-02 -0.510 0.6099
## stat125 6.534e-02 7.260e-02 0.900 0.3682
## stat126 3.494e-02 7.214e-02 0.484 0.6282
## stat127 4.698e-03 7.183e-02 0.065 0.9479
## stat128 3.332e-03 7.196e-02 0.046 0.9631
## stat129 1.105e-02 7.179e-02 0.154 0.8777
## stat130 6.539e-02 7.268e-02 0.900 0.3683
## stat131 1.066e-01 7.252e-02 1.470 0.1417
## stat132 -7.599e-02 7.196e-02 -1.056 0.2910
## stat133 2.264e-02 7.223e-02 0.313 0.7540
## stat134 -9.044e-02 7.202e-02 -1.256 0.2092
## stat135 -3.221e-02 7.231e-02 -0.445 0.6561
## stat136 3.977e-04 7.238e-02 0.005 0.9956
## stat137 2.659e-02 7.184e-02 0.370 0.7113
## stat138 2.835e-02 7.237e-02 0.392 0.6952
## stat139 3.644e-03 7.244e-02 0.050 0.9599
## stat140 -7.415e-02 7.198e-02 -1.030 0.3030
## stat141 5.894e-02 7.170e-02 0.822 0.4111
## stat142 1.641e-02 7.277e-02 0.225 0.8216
## stat143 4.762e-02 7.220e-02 0.660 0.5096
## stat144 1.126e-01 7.153e-02 1.574 0.1156
## stat145 2.442e-02 7.318e-02 0.334 0.7386
## stat146 -6.109e-02 7.261e-02 -0.841 0.4002
## stat147 -7.860e-02 7.304e-02 -1.076 0.2819
## stat148 -1.082e-01 7.127e-02 -1.519 0.1289
## stat149 -1.342e-01 7.239e-02 -1.853 0.0639 .
## stat150 1.945e-02 7.257e-02 0.268 0.7887
## stat151 -6.393e-02 7.344e-02 -0.870 0.3841
## stat152 -7.665e-02 7.227e-02 -1.061 0.2889
## stat153 6.845e-02 7.330e-02 0.934 0.3504
## stat154 -6.556e-02 7.284e-02 -0.900 0.3681
## stat155 -5.677e-02 7.203e-02 -0.788 0.4306
## stat156 1.738e-01 7.262e-02 2.393 0.0167 *
## stat157 1.333e-02 7.198e-02 0.185 0.8531
## stat158 -8.631e-02 7.315e-02 -1.180 0.2381
## stat159 -3.835e-02 7.183e-02 -0.534 0.5934
## stat160 -5.616e-03 7.269e-02 -0.077 0.9384
## stat161 1.076e-01 7.276e-02 1.479 0.1392
## stat162 -3.696e-02 7.173e-02 -0.515 0.6064
## stat163 1.492e-02 7.321e-02 0.204 0.8386
## stat164 7.082e-02 7.238e-02 0.978 0.3279
## stat165 -4.572e-02 7.190e-02 -0.636 0.5249
## stat166 -7.083e-02 7.161e-02 -0.989 0.3227
## stat167 -8.499e-02 7.202e-02 -1.180 0.2380
## stat168 -2.496e-02 7.239e-02 -0.345 0.7302
## stat169 -5.353e-02 7.254e-02 -0.738 0.4606
## stat170 -4.438e-02 7.215e-02 -0.615 0.5386
## stat171 -1.012e-02 7.268e-02 -0.139 0.8893
## stat172 9.468e-02 7.230e-02 1.310 0.1904
## stat173 -1.161e-02 7.231e-02 -0.161 0.8725
## stat174 4.847e-03 7.213e-02 0.067 0.9464
## stat175 -6.252e-02 7.275e-02 -0.859 0.3901
## stat176 -5.332e-02 7.206e-02 -0.740 0.4593
## stat177 -4.888e-02 7.276e-02 -0.672 0.5018
## stat178 1.334e-02 7.348e-02 0.181 0.8560
## stat179 -2.624e-02 7.165e-02 -0.366 0.7142
## stat180 -1.097e-01 7.169e-02 -1.531 0.1259
## stat181 6.825e-02 7.242e-02 0.942 0.3460
## stat182 5.553e-02 7.227e-02 0.768 0.4423
## stat183 1.354e-02 7.213e-02 0.188 0.8511
## stat184 1.788e-02 7.277e-02 0.246 0.8060
## stat185 -1.339e-02 7.144e-02 -0.187 0.8514
## stat186 1.006e-02 7.270e-02 0.138 0.8899
## stat187 -1.371e-01 7.198e-02 -1.905 0.0569 .
## stat188 -1.827e-02 7.190e-02 -0.254 0.7994
## stat189 2.075e-02 7.234e-02 0.287 0.7742
## stat190 3.291e-02 7.161e-02 0.460 0.6458
## stat191 -1.161e-01 7.226e-02 -1.607 0.1081
## stat192 2.780e-02 7.307e-02 0.380 0.7036
## stat193 -6.326e-02 7.287e-02 -0.868 0.3853
## stat194 1.191e-03 7.215e-02 0.017 0.9868
## stat195 1.032e-01 7.264e-02 1.421 0.1555
## stat196 3.484e-02 7.300e-02 0.477 0.6332
## stat197 -2.635e-02 7.139e-02 -0.369 0.7120
## stat198 -8.211e-02 7.219e-02 -1.137 0.2554
## stat199 1.566e-02 7.147e-02 0.219 0.8266
## stat200 -1.121e-01 7.132e-02 -1.572 0.1160
## stat201 -1.046e-02 7.196e-02 -0.145 0.8844
## stat202 -3.204e-02 7.317e-02 -0.438 0.6615
## stat203 3.601e-02 7.228e-02 0.498 0.6183
## stat204 -1.459e-01 7.237e-02 -2.016 0.0438 *
## stat205 -1.071e-01 7.212e-02 -1.486 0.1374
## stat206 -7.380e-02 7.289e-02 -1.012 0.3114
## stat207 6.258e-02 7.278e-02 0.860 0.3899
## stat208 2.710e-03 7.241e-02 0.037 0.9701
## stat209 -6.125e-03 7.185e-02 -0.085 0.9321
## stat210 -4.706e-02 7.277e-02 -0.647 0.5178
## stat211 -8.584e-02 7.216e-02 -1.190 0.2342
## stat212 5.294e-02 7.245e-02 0.731 0.4650
## stat213 -6.166e-02 7.230e-02 -0.853 0.3937
## stat214 -1.186e-01 7.224e-02 -1.642 0.1006
## stat215 -4.013e-02 7.257e-02 -0.553 0.5803
## stat216 -2.120e-02 7.221e-02 -0.294 0.7690
## stat217 7.815e-02 7.247e-02 1.078 0.2809
## sqrt.x18 7.569e+00 2.760e-01 27.428 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.501 on 5761 degrees of freedom
## Multiple R-squared: 0.2523, Adjusted R-squared: 0.2211
## F-statistic: 8.099 on 240 and 5761 DF, p-value: < 2.2e-16cd.full = plot.diagnostics(model.full, data.train)
## [1] "Number of data points that have Cook's D > 4/n: 288"
## [1] "Number of data points that have Cook's D > 1: 0"Checking with removal of high influence points
high.cd = names(cd.full[cd.full > 4/nrow(data.train)])
data.train2 = data.train[!(rownames(data.train)) %in% high.cd,]
model.full2 = lm(formula , data.train2)
summary(model.full2)##
## Call:
## lm(formula = formula, data = data.train2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.944 -5.110 -1.018 4.491 21.381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.679e+01 2.191e+00 39.603 < 2e-16 ***
## x1 -1.500e-01 1.503e-01 -0.998 0.318311
## x2 8.684e-02 9.609e-02 0.904 0.366197
## x3 1.217e-02 2.627e-02 0.463 0.643028
## x4 -1.550e-02 2.080e-03 -7.454 1.04e-13 ***
## x5 1.173e-01 6.814e-02 1.721 0.085289 .
## x6 -1.390e-01 1.374e-01 -1.011 0.311873
## x7 3.459e+00 1.454e-01 23.788 < 2e-16 ***
## x8 1.427e-01 3.401e-02 4.196 2.76e-05 ***
## x9 9.024e-01 7.598e-02 11.877 < 2e-16 ***
## x10 4.758e-01 7.070e-02 6.730 1.87e-11 ***
## x11 5.413e+07 1.699e+07 3.187 0.001447 **
## x12 1.936e-02 4.311e-02 0.449 0.653403
## x13 3.512e-02 1.732e-02 2.028 0.042583 *
## x14 -8.882e-02 7.443e-02 -1.193 0.232793
## x15 4.050e-02 7.107e-02 0.570 0.568755
## x16 2.936e-01 4.893e-02 6.001 2.09e-09 ***
## x17 4.104e-01 7.448e-02 5.510 3.75e-08 ***
## x19 2.934e-02 3.814e-02 0.769 0.441719
## x20 -2.082e-01 2.641e-01 -0.788 0.430444
## x21 2.900e-02 9.742e-03 2.977 0.002923 **
## x22 -1.184e-01 7.959e-02 -1.488 0.136889
## x23 2.442e-02 7.581e-02 0.322 0.747351
## stat1 -1.024e-01 5.711e-02 -1.793 0.073032 .
## stat2 6.227e-02 5.685e-02 1.095 0.273465
## stat3 1.285e-01 5.712e-02 2.249 0.024554 *
## stat4 -1.645e-01 5.707e-02 -2.882 0.003966 **
## stat5 -4.289e-02 5.762e-02 -0.744 0.456722
## stat6 -1.465e-01 5.714e-02 -2.564 0.010368 *
## stat7 -3.387e-02 5.691e-02 -0.595 0.551790
## stat8 -3.905e-02 5.696e-02 -0.685 0.493066
## stat9 -2.027e-02 5.708e-02 -0.355 0.722530
## stat10 -1.196e-01 5.701e-02 -2.098 0.035920 *
## stat11 -8.443e-02 5.767e-02 -1.464 0.143265
## stat12 2.330e-02 5.649e-02 0.412 0.680022
## stat13 -9.209e-02 5.695e-02 -1.617 0.105923
## stat14 -2.537e-01 5.668e-02 -4.475 7.78e-06 ***
## stat15 -1.258e-01 5.666e-02 -2.220 0.026442 *
## stat16 -6.163e-02 5.674e-02 -1.086 0.277438
## stat17 4.362e-03 5.666e-02 0.077 0.938630
## stat18 -5.829e-02 5.673e-02 -1.027 0.304288
## stat19 5.289e-02 5.675e-02 0.932 0.351405
## stat20 2.106e-02 5.695e-02 0.370 0.711525
## stat21 -6.171e-02 5.741e-02 -1.075 0.282452
## stat22 -9.004e-02 5.693e-02 -1.581 0.113822
## stat23 1.550e-01 5.707e-02 2.716 0.006635 **
## stat24 -1.222e-01 5.721e-02 -2.136 0.032713 *
## stat25 -7.666e-02 5.709e-02 -1.343 0.179456
## stat26 -1.008e-01 5.677e-02 -1.775 0.075940 .
## stat27 -4.608e-02 5.733e-02 -0.804 0.421553
## stat28 1.798e-03 5.720e-02 0.031 0.974929
## stat29 5.082e-02 5.722e-02 0.888 0.374425
## stat30 9.406e-03 5.754e-02 0.163 0.870148
## stat31 5.524e-03 5.745e-02 0.096 0.923402
## stat32 4.145e-03 5.763e-02 0.072 0.942654
## stat33 -1.700e-01 5.697e-02 -2.985 0.002853 **
## stat34 3.675e-02 5.713e-02 0.643 0.520095
## stat35 -1.555e-01 5.725e-02 -2.715 0.006640 **
## stat36 1.171e-03 5.669e-02 0.021 0.983520
## stat37 -9.930e-02 5.763e-02 -1.723 0.084920 .
## stat38 1.825e-01 5.715e-02 3.193 0.001415 **
## stat39 -1.346e-01 5.682e-02 -2.369 0.017871 *
## stat40 1.390e-02 5.695e-02 0.244 0.807196
## stat41 -1.537e-01 5.627e-02 -2.732 0.006322 **
## stat42 -7.797e-02 5.697e-02 -1.369 0.171192
## stat43 -8.180e-02 5.718e-02 -1.431 0.152590
## stat44 2.924e-02 5.682e-02 0.515 0.606841
## stat45 -7.597e-02 5.723e-02 -1.328 0.184389
## stat46 3.753e-02 5.767e-02 0.651 0.515295
## stat47 9.743e-02 5.738e-02 1.698 0.089580 .
## stat48 -7.181e-03 5.696e-02 -0.126 0.899685
## stat49 8.503e-03 5.666e-02 0.150 0.880709
## stat50 1.242e-01 5.673e-02 2.190 0.028555 *
## stat51 5.810e-02 5.682e-02 1.022 0.306601
## stat52 -1.531e-02 5.717e-02 -0.268 0.788942
## stat53 -1.343e-02 5.745e-02 -0.234 0.815225
## stat54 -8.824e-02 5.742e-02 -1.537 0.124395
## stat55 1.427e-02 5.658e-02 0.252 0.800900
## stat56 5.096e-02 5.724e-02 0.890 0.373387
## stat57 -1.108e-02 5.684e-02 -0.195 0.845486
## stat58 2.446e-03 5.647e-02 0.043 0.965454
## stat59 6.050e-02 5.681e-02 1.065 0.286969
## stat60 1.087e-01 5.713e-02 1.903 0.057148 .
## stat61 -3.057e-02 5.690e-02 -0.537 0.591049
## stat62 -8.544e-02 5.665e-02 -1.508 0.131562
## stat63 8.721e-02 5.714e-02 1.526 0.126998
## stat64 1.802e-02 5.647e-02 0.319 0.749650
## stat65 -7.029e-02 5.729e-02 -1.227 0.219903
## stat66 6.766e-02 5.787e-02 1.169 0.242328
## stat67 5.983e-02 5.743e-02 1.042 0.297548
## stat68 -4.282e-02 5.706e-02 -0.750 0.453030
## stat69 -1.662e-02 5.669e-02 -0.293 0.769344
## stat70 5.096e-02 5.681e-02 0.897 0.369770
## stat71 4.276e-02 5.691e-02 0.751 0.452461
## stat72 5.734e-02 5.744e-02 0.998 0.318196
## stat73 8.509e-02 5.731e-02 1.485 0.137688
## stat74 1.927e-02 5.724e-02 0.337 0.736400
## stat75 1.267e-02 5.743e-02 0.221 0.825446
## stat76 -7.028e-03 5.700e-02 -0.123 0.901876
## stat77 5.681e-03 5.715e-02 0.099 0.920825
## stat78 -1.058e-01 5.708e-02 -1.854 0.063750 .
## stat79 6.197e-02 5.695e-02 1.088 0.276538
## stat80 7.615e-02 5.717e-02 1.332 0.182900
## stat81 6.029e-02 5.727e-02 1.053 0.292453
## stat82 2.419e-02 5.712e-02 0.424 0.671871
## stat83 -2.493e-03 5.706e-02 -0.044 0.965151
## stat84 -9.260e-02 5.708e-02 -1.622 0.104800
## stat85 -5.591e-02 5.714e-02 -0.978 0.327905
## stat86 3.508e-02 5.730e-02 0.612 0.540409
## stat87 -5.425e-02 5.714e-02 -0.949 0.342469
## stat88 -1.029e-02 5.693e-02 -0.181 0.856543
## stat89 -4.040e-02 5.697e-02 -0.709 0.478219
## stat90 -5.604e-02 5.716e-02 -0.980 0.326991
## stat91 -8.406e-02 5.663e-02 -1.484 0.137759
## stat92 -9.160e-02 5.685e-02 -1.611 0.107164
## stat93 -2.111e-02 5.794e-02 -0.364 0.715625
## stat94 2.765e-02 5.732e-02 0.482 0.629516
## stat95 8.477e-02 5.691e-02 1.490 0.136411
## stat96 -3.339e-02 5.693e-02 -0.586 0.557597
## stat97 -1.299e-02 5.659e-02 -0.229 0.818504
## stat98 9.648e-01 5.631e-02 17.133 < 2e-16 ***
## stat99 5.991e-02 5.725e-02 1.046 0.295393
## stat100 1.826e-01 5.718e-02 3.194 0.001410 **
## stat101 -3.588e-02 5.769e-02 -0.622 0.534033
## stat102 2.298e-02 5.729e-02 0.401 0.688429
## stat103 -7.130e-02 5.754e-02 -1.239 0.215291
## stat104 -1.423e-02 5.695e-02 -0.250 0.802730
## stat105 8.695e-02 5.661e-02 1.536 0.124648
## stat106 -1.086e-01 5.685e-02 -1.910 0.056131 .
## stat107 -5.293e-02 5.720e-02 -0.925 0.354886
## stat108 -1.829e-02 5.711e-02 -0.320 0.748853
## stat109 -3.427e-02 5.723e-02 -0.599 0.549301
## stat110 -9.074e-01 5.671e-02 -16.001 < 2e-16 ***
## stat111 4.466e-02 5.698e-02 0.784 0.433211
## stat112 1.395e-02 5.746e-02 0.243 0.808223
## stat113 9.215e-03 5.746e-02 0.160 0.872598
## stat114 4.348e-02 5.750e-02 0.756 0.449572
## stat115 1.141e-01 5.710e-02 1.998 0.045818 *
## stat116 8.528e-02 5.767e-02 1.479 0.139225
## stat117 7.156e-02 5.714e-02 1.252 0.210503
## stat118 1.547e-02 5.668e-02 0.273 0.784843
## stat119 1.061e-01 5.686e-02 1.867 0.062020 .
## stat120 2.504e-02 5.662e-02 0.442 0.658382
## stat121 -3.080e-02 5.738e-02 -0.537 0.591473
## stat122 -6.132e-02 5.685e-02 -1.079 0.280775
## stat123 9.642e-02 5.759e-02 1.674 0.094143 .
## stat124 -4.151e-02 5.679e-02 -0.731 0.464897
## stat125 1.286e-02 5.736e-02 0.224 0.822595
## stat126 -6.164e-03 5.699e-02 -0.108 0.913879
## stat127 -3.064e-02 5.671e-02 -0.540 0.589048
## stat128 -4.420e-02 5.675e-02 -0.779 0.436111
## stat129 1.930e-03 5.655e-02 0.034 0.972775
## stat130 4.099e-02 5.740e-02 0.714 0.475217
## stat131 2.397e-02 5.714e-02 0.419 0.674917
## stat132 -8.747e-02 5.677e-02 -1.541 0.123431
## stat133 6.462e-02 5.719e-02 1.130 0.258578
## stat134 -6.097e-02 5.682e-02 -1.073 0.283366
## stat135 -2.843e-02 5.712e-02 -0.498 0.618685
## stat136 -1.254e-02 5.706e-02 -0.220 0.826064
## stat137 8.410e-02 5.666e-02 1.484 0.137810
## stat138 4.378e-02 5.719e-02 0.766 0.443952
## stat139 -6.446e-03 5.720e-02 -0.113 0.910274
## stat140 -6.005e-02 5.672e-02 -1.059 0.289810
## stat141 8.710e-02 5.661e-02 1.539 0.123942
## stat142 2.936e-02 5.746e-02 0.511 0.609369
## stat143 -5.725e-03 5.705e-02 -0.100 0.920066
## stat144 1.298e-01 5.639e-02 2.303 0.021339 *
## stat145 -2.276e-02 5.789e-02 -0.393 0.694209
## stat146 -9.056e-02 5.732e-02 -1.580 0.114201
## stat147 -6.916e-02 5.772e-02 -1.198 0.230932
## stat148 -1.046e-01 5.626e-02 -1.859 0.063142 .
## stat149 -1.828e-01 5.722e-02 -3.195 0.001406 **
## stat150 -2.510e-02 5.741e-02 -0.437 0.661991
## stat151 9.972e-03 5.813e-02 0.172 0.863806
## stat152 -5.857e-02 5.698e-02 -1.028 0.304071
## stat153 7.057e-02 5.787e-02 1.220 0.222683
## stat154 5.373e-03 5.761e-02 0.093 0.925696
## stat155 -8.870e-03 5.690e-02 -0.156 0.876122
## stat156 1.945e-01 5.726e-02 3.396 0.000688 ***
## stat157 2.870e-02 5.678e-02 0.505 0.613251
## stat158 -4.424e-04 5.771e-02 -0.008 0.993884
## stat159 1.379e-02 5.679e-02 0.243 0.808121
## stat160 4.656e-03 5.748e-02 0.081 0.935444
## stat161 5.455e-02 5.745e-02 0.949 0.342461
## stat162 -5.137e-02 5.653e-02 -0.909 0.363503
## stat163 1.131e-02 5.784e-02 0.196 0.844967
## stat164 2.228e-02 5.725e-02 0.389 0.697192
## stat165 -3.964e-02 5.679e-02 -0.698 0.485139
## stat166 -6.409e-02 5.647e-02 -1.135 0.256490
## stat167 -1.055e-01 5.682e-02 -1.856 0.063511 .
## stat168 -3.610e-02 5.706e-02 -0.633 0.527035
## stat169 -6.499e-02 5.744e-02 -1.131 0.257958
## stat170 -8.522e-03 5.696e-02 -0.150 0.881064
## stat171 -4.750e-02 5.746e-02 -0.827 0.408421
## stat172 1.661e-01 5.694e-02 2.918 0.003541 **
## stat173 1.254e-02 5.706e-02 0.220 0.826084
## stat174 7.481e-02 5.692e-02 1.314 0.188753
## stat175 -4.793e-02 5.739e-02 -0.835 0.403700
## stat176 -1.166e-01 5.687e-02 -2.051 0.040323 *
## stat177 -9.202e-02 5.741e-02 -1.603 0.109035
## stat178 2.773e-02 5.800e-02 0.478 0.632592
## stat179 -5.187e-02 5.651e-02 -0.918 0.358641
## stat180 -6.484e-02 5.670e-02 -1.144 0.252839
## stat181 9.516e-02 5.712e-02 1.666 0.095791 .
## stat182 9.005e-02 5.713e-02 1.576 0.115022
## stat183 3.572e-03 5.710e-02 0.063 0.950117
## stat184 9.258e-02 5.750e-02 1.610 0.107438
## stat185 2.740e-03 5.644e-02 0.049 0.961287
## stat186 7.015e-02 5.752e-02 1.219 0.222716
## stat187 -6.206e-02 5.682e-02 -1.092 0.274752
## stat188 -2.156e-02 5.674e-02 -0.380 0.703942
## stat189 -1.877e-02 5.726e-02 -0.328 0.743088
## stat190 -1.223e-02 5.661e-02 -0.216 0.829015
## stat191 -9.372e-02 5.689e-02 -1.647 0.099565 .
## stat192 3.588e-02 5.787e-02 0.620 0.535303
## stat193 1.610e-02 5.759e-02 0.280 0.779806
## stat194 -2.569e-02 5.703e-02 -0.450 0.652402
## stat195 2.683e-02 5.743e-02 0.467 0.640376
## stat196 -6.189e-03 5.762e-02 -0.107 0.914465
## stat197 -5.689e-02 5.644e-02 -1.008 0.313517
## stat198 -5.718e-02 5.698e-02 -1.004 0.315621
## stat199 3.639e-02 5.647e-02 0.644 0.519319
## stat200 -7.932e-02 5.641e-02 -1.406 0.159729
## stat201 3.153e-02 5.693e-02 0.554 0.579654
## stat202 8.662e-03 5.779e-02 0.150 0.880849
## stat203 3.070e-02 5.705e-02 0.538 0.590435
## stat204 -5.890e-02 5.714e-02 -1.031 0.302714
## stat205 3.088e-03 5.678e-02 0.054 0.956630
## stat206 -9.447e-02 5.747e-02 -1.644 0.100287
## stat207 2.613e-02 5.754e-02 0.454 0.649747
## stat208 6.176e-02 5.734e-02 1.077 0.281518
## stat209 1.311e-02 5.668e-02 0.231 0.817100
## stat210 -1.033e-01 5.742e-02 -1.799 0.072074 .
## stat211 -5.829e-02 5.702e-02 -1.022 0.306634
## stat212 5.304e-02 5.722e-02 0.927 0.353958
## stat213 -7.850e-02 5.706e-02 -1.376 0.168926
## stat214 -7.612e-02 5.708e-02 -1.334 0.182400
## stat215 -3.828e-02 5.734e-02 -0.668 0.504435
## stat216 -4.636e-02 5.698e-02 -0.814 0.415877
## stat217 2.588e-02 5.713e-02 0.453 0.650605
## sqrt.x18 7.391e+00 2.170e-01 34.054 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.312 on 5473 degrees of freedom
## Multiple R-squared: 0.3573, Adjusted R-squared: 0.3291
## F-statistic: 12.68 on 240 and 5473 DF, p-value: < 2.2e-16cd.full2 = plot.diagnostics(model.full2, data.train2)
## [1] "Number of data points that have Cook's D > 4/n: 321"
## [1] "Number of data points that have Cook's D > 1: 0"# much more normal residuals than before.
# Checking to see if distributions are different and if so whcih variables
# High Leverage Plot
plotData = data.train %>%
rownames_to_column() %>%
mutate(type=ifelse(rowname %in% high.cd,'High','Normal')) %>%
dplyr::select(type,target=one_of(label.names))
ggplot(data=plotData, aes(x=type,y=target)) +
geom_boxplot(fill='light blue',outlier.shape=NA) +
scale_y_continuous(name="Target Variable Values") +
theme_light() +
ggtitle('Distribution of High Leverage Points and Normal Points')
plotData = data.train %>%
rownames_to_column() %>%
mutate(type=ifelse(rowname %in% high.cd,'High','Normal')) %>%
dplyr::select(type,one_of(feature.names))# 2 sample t-tests
comp.test = lapply(dplyr::select(plotData, one_of(feature.names)), function(x) t.test(x ~ plotData$type, var.equal = TRUE))
sig.comp = list.filter(comp.test, p.value < 0.05)
sapply(sig.comp, function(x) x[['p.value']])## stat64 stat67 stat82 stat93 stat98
## 3.437616e-02 4.380049e-02 7.575191e-03 1.885805e-02 3.609357e-05
## stat110 stat146 stat214 sqrt.x18
## 4.617328e-04 3.659329e-02 3.341761e-02 2.352371e-03# Distribution (box) Plots
mm = melt(plotData, id=c('type'))
ggplot(mm) +
geom_boxplot(aes(x=type, y=value))+
facet_wrap(~variable, ncol=10, scales = 'free') +
ggtitle('Distribution of High Leverage Points and Normal Points')
ggsave('comparison.jpeg', width =50, height = 400, units='cm',limitsize = FALSE)Grand Means Model
model.null = lm(grand.mean.formula, data.train)model.null2 = lm(grand.mean.formula, data.train2)Variable Selection
Basic: http://www.stat.columbia.edu/~martin/W2024/R10.pdf Cross Validation + Other Metrics: http://www.sthda.com/english/articles/37-model-selection-essentials-in-r/154-stepwise-regression-essentials-in-r/
Forward Selection (w/ full train)
Train
if (algo.forward == TRUE){
t1 = Sys.time()
model.forward = step(model.null, scope=list(lower=model.null, upper=model.full), direction="forward", trace = 0)
print(summary(model.forward))
t2 = Sys.time()
print (paste("Time taken for Forward Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.forward, data.train)
}Test
if (algo.forward == TRUE){
test.model(model.forward, data.test, "Forward Selection")
}Forward Selection (w/ filtered train)
Train
if (algo.forward == TRUE){
t1 = Sys.time()
model.forward2 = step(model.null2, scope=list(lower=model.null2, upper=model.full2), direction="forward", trace = 0)
print(summary(model.forward2))
t2 = Sys.time()
print (paste("Time taken for Forward Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.forward2, data.train2)
}Test
if (algo.forward == TRUE){
test.model(model.forward2, data.test, "Forward Selection (2)")
}Forward Selection with CV (w/ full train)
Train
if (algo.forward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
, data = data.train
, method = "leapForward"
, feature.names = feature.names)
model.forward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 9 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 10.210693 0.1018765 7.806589 0.3486064 0.02595312 0.1962709
## 2 2 9.979515 0.1418363 7.597398 0.3207096 0.02875352 0.1777050
## 3 3 9.814897 0.1693658 7.438338 0.3290643 0.02785753 0.1807549
## 4 4 9.667493 0.1938902 7.231990 0.3201540 0.02682717 0.1803033
## 5 5 9.588875 0.2068466 7.172167 0.3419604 0.02484619 0.1975346
## 6 6 9.591660 0.2066208 7.174795 0.3521685 0.02739708 0.2020679
## 7 7 9.571737 0.2099116 7.168707 0.3525200 0.02833739 0.2076171
## 8 8 9.546348 0.2140867 7.150908 0.3571826 0.02810121 0.2058362
## 9 9 9.521497 0.2180754 7.135342 0.3515932 0.02753943 0.1919503
## 10 10 9.531634 0.2164981 7.142145 0.3502059 0.02798899 0.1947027
## 11 11 9.531499 0.2165176 7.142129 0.3493156 0.02735269 0.1916070
## 12 12 9.541275 0.2149978 7.148145 0.3511383 0.02743081 0.1879036
## 13 13 9.539867 0.2151993 7.145155 0.3456114 0.02644858 0.1873780
## 14 14 9.532611 0.2163848 7.137358 0.3470188 0.02761253 0.1915646
## 15 15 9.525866 0.2174740 7.126506 0.3478493 0.02822187 0.1955673
## 16 16 9.532841 0.2163902 7.129214 0.3525212 0.02855943 0.1972010
## 17 17 9.532361 0.2164472 7.126063 0.3482723 0.02779395 0.1925486
## 18 18 9.535834 0.2159596 7.133904 0.3474052 0.02771944 0.1957735
## 19 19 9.535883 0.2159502 7.135435 0.3475181 0.02829213 0.1958929
## 20 20 9.537108 0.2157991 7.133816 0.3489418 0.02882166 0.1925465
## 21 21 9.539986 0.2153852 7.134572 0.3543507 0.02926639 0.1906264
## 22 22 9.545096 0.2146096 7.139438 0.3574050 0.02982504 0.1984879
## 23 23 9.548369 0.2140656 7.141456 0.3559449 0.02965578 0.1939936
## 24 24 9.556574 0.2127700 7.149307 0.3553712 0.02886227 0.1889438
## 25 25 9.560161 0.2122506 7.154158 0.3584403 0.02911492 0.1868420
## 26 26 9.567531 0.2110771 7.161210 0.3559550 0.02891696 0.1891417
## 27 27 9.568763 0.2108701 7.163947 0.3543281 0.02851222 0.1878238
## 28 28 9.578408 0.2094001 7.166077 0.3544061 0.02808319 0.1866707
## 29 29 9.584943 0.2084431 7.172753 0.3576149 0.02858654 0.1893383
## 30 30 9.590321 0.2076733 7.173134 0.3634458 0.02876178 0.1965202
## 31 31 9.596942 0.2066920 7.176744 0.3634694 0.02889365 0.1994013
## 32 32 9.602009 0.2059721 7.183235 0.3663653 0.02903096 0.2058716
## 33 33 9.605377 0.2054437 7.188016 0.3651406 0.02880960 0.2101985
## 34 34 9.608019 0.2050839 7.189678 0.3650497 0.02875688 0.2131259
## 35 35 9.609675 0.2048133 7.190762 0.3564514 0.02785446 0.2072426
## 36 36 9.613063 0.2043122 7.194013 0.3567571 0.02771453 0.2094076
## 37 37 9.619848 0.2032673 7.201187 0.3571364 0.02729727 0.2085368
## 38 38 9.625883 0.2023755 7.204602 0.3537499 0.02711129 0.2025209
## 39 39 9.628373 0.2020049 7.207561 0.3513626 0.02665465 0.2057779
## 40 40 9.632131 0.2014506 7.209335 0.3514043 0.02691156 0.2068670
## 41 41 9.632626 0.2013750 7.208457 0.3522246 0.02684180 0.2095138
## 42 42 9.635143 0.2010236 7.211166 0.3557478 0.02693283 0.2143238
## 43 43 9.639155 0.2004534 7.215106 0.3590516 0.02719877 0.2117256
## 44 44 9.644516 0.1996461 7.220196 0.3569259 0.02692388 0.2083983
## 45 45 9.646351 0.1994383 7.222689 0.3562603 0.02736089 0.2107924
## 46 46 9.650263 0.1988711 7.223454 0.3575405 0.02759603 0.2141574
## 47 47 9.653936 0.1983459 7.225812 0.3552781 0.02769683 0.2116223
## 48 48 9.656438 0.1980132 7.227800 0.3572046 0.02837399 0.2111106
## 49 49 9.661297 0.1972873 7.230177 0.3584694 0.02841858 0.2156499
## 50 50 9.661678 0.1973108 7.232656 0.3610208 0.02896234 0.2197475
## 51 51 9.663216 0.1970700 7.235766 0.3605234 0.02895781 0.2239816
## 52 52 9.666690 0.1965300 7.237825 0.3565754 0.02875953 0.2189952
## 53 53 9.669841 0.1960224 7.240503 0.3578076 0.02827219 0.2172610
## 54 54 9.672332 0.1957025 7.243661 0.3596879 0.02840620 0.2167286
## 55 55 9.676210 0.1951180 7.247522 0.3572600 0.02808822 0.2125986
## 56 56 9.678724 0.1947529 7.251337 0.3586957 0.02810280 0.2174194
## 57 57 9.679344 0.1947004 7.250862 0.3571636 0.02776301 0.2168266
## 58 58 9.683005 0.1941772 7.252193 0.3574051 0.02749202 0.2160766
## 59 59 9.683791 0.1940636 7.254804 0.3552678 0.02764702 0.2123418
## 60 60 9.681709 0.1943979 7.251209 0.3552790 0.02784421 0.2102172
## 61 61 9.684873 0.1939576 7.254581 0.3555684 0.02816321 0.2117349
## 62 62 9.684143 0.1940724 7.256817 0.3558320 0.02767953 0.2098835
## 63 63 9.687365 0.1936444 7.257991 0.3549303 0.02746551 0.2078838
## 64 64 9.688831 0.1934956 7.259952 0.3578128 0.02808321 0.2105320
## 65 65 9.688023 0.1935787 7.258746 0.3574163 0.02782399 0.2126790
## 66 66 9.689957 0.1933137 7.259252 0.3580896 0.02741208 0.2133409
## 67 67 9.689771 0.1933961 7.257998 0.3602286 0.02771117 0.2145943
## 68 68 9.691509 0.1931944 7.257951 0.3592407 0.02790230 0.2111481
## 69 69 9.691488 0.1932008 7.257627 0.3559450 0.02750716 0.2088413
## 70 70 9.689064 0.1935861 7.256186 0.3555813 0.02770565 0.2073898
## 71 71 9.689612 0.1935533 7.255781 0.3589698 0.02827682 0.2091546
## 72 72 9.689917 0.1935373 7.255957 0.3607968 0.02810326 0.2095896
## 73 73 9.688082 0.1938077 7.256937 0.3613263 0.02790771 0.2093251
## 74 74 9.689396 0.1935915 7.258453 0.3586836 0.02756373 0.2062293
## 75 75 9.688879 0.1937038 7.258113 0.3549547 0.02716287 0.2022783
## 76 76 9.691189 0.1933690 7.259718 0.3527558 0.02700823 0.1987957
## 77 77 9.691443 0.1933204 7.259059 0.3515241 0.02717847 0.1994354
## 78 78 9.687462 0.1939407 7.256122 0.3523742 0.02742099 0.2030728
## 79 79 9.686008 0.1941786 7.255603 0.3533218 0.02734732 0.2044142
## 80 80 9.689984 0.1936037 7.258016 0.3501682 0.02697875 0.2018042
## 81 81 9.690150 0.1936158 7.258779 0.3529217 0.02742064 0.2025384
## 82 82 9.690250 0.1935989 7.257515 0.3518228 0.02743420 0.2008556
## 83 83 9.693437 0.1931468 7.260294 0.3530499 0.02732938 0.2009855
## 84 84 9.693501 0.1931732 7.260473 0.3561392 0.02736843 0.2031897
## 85 85 9.693804 0.1931537 7.262102 0.3551883 0.02739868 0.2048698
## 86 86 9.694236 0.1931039 7.263032 0.3550944 0.02718449 0.2042880
## 87 87 9.692127 0.1934310 7.260169 0.3529923 0.02693648 0.2032702
## 88 88 9.692059 0.1934602 7.261792 0.3559334 0.02753307 0.2063066
## 89 89 9.693638 0.1932626 7.264383 0.3531071 0.02735124 0.2032762
## 90 90 9.695486 0.1929860 7.265482 0.3514273 0.02705292 0.2016088
## 91 91 9.695773 0.1928992 7.266406 0.3507129 0.02674625 0.2022263
## 92 92 9.694417 0.1931147 7.264096 0.3513644 0.02701991 0.2016721
## 93 93 9.697373 0.1927257 7.266673 0.3498407 0.02690686 0.1989564
## 94 94 9.694635 0.1931139 7.266569 0.3509674 0.02692018 0.1994855
## 95 95 9.695091 0.1930696 7.267448 0.3521849 0.02719542 0.2012604
## 96 96 9.695178 0.1930754 7.267516 0.3522821 0.02753624 0.2026018
## 97 97 9.692798 0.1934469 7.264351 0.3512426 0.02757329 0.2021689
## 98 98 9.692407 0.1934860 7.265424 0.3481302 0.02742818 0.2013792
## 99 99 9.691711 0.1936090 7.267589 0.3462840 0.02713372 0.1999290
## 100 100 9.691441 0.1935999 7.266659 0.3447557 0.02691220 0.1991989
## 101 101 9.691664 0.1935443 7.266953 0.3441838 0.02670262 0.1992938
## 102 102 9.694980 0.1930677 7.268472 0.3450903 0.02683975 0.2001116
## 103 103 9.694546 0.1931565 7.267390 0.3470629 0.02687205 0.2026612
## 104 104 9.694703 0.1931350 7.267185 0.3463995 0.02671831 0.2016367
## 105 105 9.694982 0.1931394 7.268128 0.3474113 0.02675444 0.2020220
## 106 106 9.692997 0.1934482 7.265571 0.3483559 0.02700362 0.2022296
## 107 107 9.693339 0.1934192 7.263105 0.3506780 0.02703746 0.2044658
## 108 108 9.694399 0.1932631 7.261911 0.3498684 0.02670262 0.2051578
## 109 109 9.694761 0.1932444 7.262043 0.3490516 0.02653802 0.2064869
## 110 110 9.693563 0.1934402 7.260259 0.3482459 0.02652027 0.2066578
## 111 111 9.695733 0.1931720 7.260721 0.3490042 0.02654383 0.2065935
## 112 112 9.699057 0.1926691 7.265924 0.3444228 0.02612708 0.2019630
## 113 113 9.699108 0.1927028 7.265524 0.3416723 0.02607880 0.2008849
## 114 114 9.700501 0.1925139 7.267376 0.3420668 0.02604139 0.2015627
## 115 115 9.700575 0.1925041 7.267046 0.3401314 0.02583579 0.1991818
## 116 116 9.701421 0.1924063 7.265952 0.3404865 0.02618439 0.2000405
## 117 117 9.701847 0.1923575 7.265523 0.3387378 0.02612060 0.1986849
## 118 118 9.701131 0.1924840 7.265311 0.3394010 0.02611516 0.1978235
## 119 119 9.701957 0.1923897 7.267469 0.3385725 0.02610626 0.1982901
## 120 120 9.698905 0.1928497 7.266861 0.3386928 0.02618241 0.1992798
## 121 121 9.700207 0.1926744 7.266861 0.3403616 0.02607734 0.1989074
## 122 122 9.699802 0.1927391 7.267443 0.3387849 0.02632963 0.1972449
## 123 123 9.698763 0.1928845 7.267285 0.3394941 0.02635248 0.1976528
## 124 124 9.699654 0.1927469 7.269303 0.3378107 0.02602415 0.1950250
## 125 125 9.700044 0.1927113 7.270595 0.3404225 0.02629957 0.1965995
## 126 126 9.698635 0.1929456 7.270336 0.3402247 0.02656200 0.1970726
## 127 127 9.699494 0.1928214 7.271363 0.3376679 0.02627093 0.1946691
## 128 128 9.698565 0.1929329 7.270181 0.3369873 0.02614222 0.1949181
## 129 129 9.698099 0.1930101 7.270023 0.3387016 0.02626693 0.1961883
## 130 130 9.698527 0.1929570 7.269079 0.3389947 0.02624710 0.1948468
## 131 131 9.699954 0.1927590 7.271706 0.3379885 0.02642737 0.1951646
## 132 132 9.702112 0.1924609 7.272163 0.3376784 0.02638499 0.1944853
## 133 133 9.702649 0.1923948 7.272820 0.3388609 0.02656596 0.1958059
## 134 134 9.700675 0.1926849 7.271818 0.3395236 0.02642825 0.1957372
## 135 135 9.700222 0.1927673 7.272235 0.3388912 0.02623423 0.1960871
## 136 136 9.700851 0.1927185 7.272453 0.3390501 0.02633402 0.1961100
## 137 137 9.701029 0.1926953 7.272269 0.3405803 0.02676330 0.1967972
## 138 138 9.701565 0.1926440 7.272729 0.3408626 0.02664153 0.1969086
## 139 139 9.704114 0.1923040 7.274296 0.3406635 0.02672079 0.1965254
## 140 140 9.703699 0.1923487 7.274084 0.3402685 0.02688528 0.1955946
## 141 141 9.702996 0.1924649 7.273427 0.3386508 0.02676303 0.1935418
## 142 142 9.704774 0.1921841 7.274815 0.3362565 0.02656328 0.1919154
## 143 143 9.706357 0.1919710 7.276729 0.3358908 0.02660394 0.1910240
## 144 144 9.704214 0.1922934 7.274991 0.3369830 0.02656617 0.1921628
## 145 145 9.705566 0.1921434 7.275978 0.3371607 0.02682012 0.1913397
## 146 146 9.706269 0.1920389 7.277068 0.3373873 0.02683822 0.1916561
## 147 147 9.705085 0.1922026 7.276425 0.3373515 0.02673055 0.1915187
## 148 148 9.704911 0.1922397 7.276552 0.3373313 0.02678738 0.1924604
## 149 149 9.707071 0.1919650 7.278475 0.3390115 0.02704366 0.1928745
## 150 150 9.706344 0.1920691 7.278001 0.3379772 0.02694983 0.1910947
## 151 151 9.706748 0.1920181 7.277168 0.3387240 0.02703103 0.1901729
## 152 152 9.706324 0.1920741 7.276622 0.3386834 0.02696635 0.1903012
## 153 153 9.704879 0.1922686 7.275758 0.3385577 0.02701028 0.1908511
## 154 154 9.705020 0.1922380 7.276089 0.3385786 0.02691514 0.1910534
## 155 155 9.704600 0.1923028 7.275738 0.3387205 0.02680884 0.1916219
## 156 156 9.704513 0.1923068 7.274916 0.3380627 0.02666329 0.1895684
## 157 157 9.704398 0.1923062 7.273772 0.3386624 0.02666397 0.1910449
## 158 158 9.704606 0.1922715 7.273908 0.3393221 0.02655669 0.1916567
## 159 159 9.706032 0.1920574 7.275359 0.3403004 0.02654980 0.1914877
## 160 160 9.704460 0.1922881 7.273292 0.3414840 0.02665290 0.1923099
## 161 161 9.705080 0.1921998 7.273535 0.3410757 0.02661807 0.1913351
## 162 162 9.704095 0.1923570 7.272853 0.3409219 0.02673132 0.1915933
## 163 163 9.703645 0.1924299 7.272162 0.3405043 0.02667306 0.1910276
## 164 164 9.703163 0.1925060 7.271736 0.3409699 0.02670420 0.1916331
## 165 165 9.703847 0.1924197 7.273117 0.3417612 0.02670882 0.1915095
## 166 166 9.704622 0.1923163 7.274054 0.3408596 0.02664589 0.1915127
## 167 167 9.704096 0.1923814 7.274649 0.3417619 0.02684851 0.1913057
## 168 168 9.703297 0.1925005 7.274374 0.3418504 0.02707423 0.1904948
## 169 169 9.703244 0.1925071 7.274468 0.3421153 0.02700240 0.1905269
## 170 170 9.702367 0.1926487 7.273260 0.3432559 0.02717293 0.1912544
## 171 171 9.702840 0.1925854 7.273152 0.3425182 0.02715790 0.1909568
## 172 172 9.702221 0.1926962 7.272604 0.3428953 0.02719244 0.1913096
## 173 173 9.701577 0.1927818 7.272618 0.3430142 0.02723474 0.1921650
## 174 174 9.700372 0.1929652 7.271121 0.3430442 0.02721216 0.1924483
## 175 175 9.701029 0.1928888 7.272035 0.3436628 0.02739819 0.1932923
## 176 176 9.701902 0.1927679 7.272780 0.3430132 0.02721065 0.1931672
## 177 177 9.701989 0.1927521 7.272228 0.3433882 0.02718574 0.1939876
## 178 178 9.702685 0.1926705 7.272749 0.3460154 0.02742260 0.1961431
## 179 179 9.703002 0.1926303 7.273621 0.3460370 0.02733456 0.1956420
## 180 180 9.703481 0.1925558 7.274308 0.3459908 0.02723305 0.1961563
## 181 181 9.703882 0.1925085 7.274385 0.3458318 0.02729889 0.1960555
## 182 182 9.705298 0.1923037 7.275209 0.3465585 0.02747757 0.1965091
## 183 183 9.704513 0.1924216 7.274921 0.3474233 0.02761610 0.1961744
## 184 184 9.704500 0.1924371 7.274731 0.3469509 0.02769948 0.1960387
## 185 185 9.704641 0.1924108 7.275130 0.3467324 0.02761264 0.1961520
## 186 186 9.704739 0.1924049 7.274526 0.3469983 0.02763660 0.1970158
## 187 187 9.704815 0.1923725 7.274728 0.3451005 0.02725401 0.1961205
## 188 188 9.704796 0.1923605 7.275212 0.3439497 0.02713628 0.1950359
## 189 189 9.704711 0.1923701 7.274616 0.3444465 0.02721944 0.1953944
## 190 190 9.705464 0.1922544 7.275405 0.3432300 0.02711096 0.1944152
## 191 191 9.706538 0.1920932 7.276061 0.3429824 0.02687769 0.1942740
## 192 192 9.707090 0.1920082 7.276367 0.3432024 0.02685902 0.1948152
## 193 193 9.707824 0.1919073 7.276448 0.3432033 0.02686022 0.1944440
## 194 194 9.707287 0.1919870 7.276099 0.3435543 0.02689076 0.1950857
## 195 195 9.707372 0.1919857 7.276107 0.3436176 0.02696579 0.1953178
## 196 196 9.706264 0.1921508 7.275739 0.3432227 0.02704117 0.1946457
## 197 197 9.706917 0.1920720 7.276033 0.3434183 0.02698429 0.1948166
## 198 198 9.706757 0.1920972 7.275784 0.3439086 0.02702790 0.1950741
## 199 199 9.706563 0.1921089 7.275653 0.3434929 0.02692172 0.1951014
## 200 200 9.707453 0.1919860 7.276681 0.3434323 0.02696871 0.1953741
## 201 201 9.706849 0.1920703 7.275752 0.3430747 0.02697143 0.1953223
## 202 202 9.707248 0.1920176 7.276412 0.3426836 0.02694603 0.1949861
## 203 203 9.706883 0.1920770 7.275996 0.3427294 0.02698645 0.1947452
## 204 204 9.706961 0.1920698 7.276315 0.3418034 0.02690694 0.1945011
## 205 205 9.707031 0.1920645 7.276134 0.3422205 0.02687238 0.1948306
## 206 206 9.707212 0.1920317 7.276559 0.3417056 0.02686176 0.1949580
## 207 207 9.707812 0.1919395 7.277206 0.3412297 0.02680738 0.1944318
## 208 208 9.707383 0.1920054 7.276927 0.3421387 0.02687391 0.1951521
## 209 209 9.707098 0.1920555 7.276526 0.3423560 0.02689591 0.1952065
## 210 210 9.706889 0.1920889 7.276527 0.3423302 0.02688841 0.1946550
## 211 211 9.707230 0.1920369 7.276482 0.3420154 0.02689284 0.1944229
## 212 212 9.707232 0.1920256 7.276565 0.3417825 0.02685283 0.1942663
## 213 213 9.707529 0.1919811 7.276811 0.3422276 0.02685810 0.1946583
## 214 214 9.706872 0.1920779 7.276312 0.3421563 0.02687518 0.1946258
## 215 215 9.707125 0.1920436 7.276537 0.3423709 0.02688269 0.1948166
## 216 216 9.707523 0.1919918 7.276899 0.3421291 0.02683683 0.1943649
## 217 217 9.707587 0.1919836 7.276913 0.3417189 0.02684910 0.1941020
## 218 218 9.707312 0.1920244 7.276848 0.3418386 0.02686413 0.1943437
## 219 219 9.707181 0.1920418 7.276845 0.3416553 0.02685859 0.1940363
## 220 220 9.707422 0.1920032 7.277065 0.3416047 0.02686831 0.1939818
## 221 221 9.707501 0.1919944 7.277178 0.3418774 0.02689742 0.1940936
## 222 222 9.707310 0.1920203 7.276914 0.3417660 0.02687880 0.1941040
## 223 223 9.707131 0.1920444 7.276929 0.3416600 0.02687449 0.1941464
## 224 224 9.707377 0.1920082 7.277091 0.3416442 0.02686254 0.1942232
## 225 225 9.707262 0.1920221 7.276725 0.3415707 0.02684678 0.1942614
## 226 226 9.707045 0.1920539 7.276607 0.3416431 0.02685931 0.1943162
## 227 227 9.706838 0.1920822 7.276457 0.3414971 0.02684527 0.1940610
## 228 228 9.706890 0.1920750 7.276668 0.3413004 0.02681223 0.1939648
## 229 229 9.706862 0.1920806 7.276604 0.3413612 0.02683954 0.1941413
## 230 230 9.706910 0.1920736 7.276613 0.3413077 0.02683709 0.1940985
## 231 231 9.706816 0.1920869 7.276530 0.3414668 0.02684573 0.1942758
## 232 232 9.706798 0.1920900 7.276554 0.3414690 0.02683953 0.1942318
## 233 233 9.706781 0.1920922 7.276498 0.3415837 0.02685977 0.1943116
## 234 234 9.706862 0.1920808 7.276539 0.3414934 0.02684693 0.1942315
## 235 235 9.706860 0.1920807 7.276577 0.3414986 0.02684323 0.1942913
## 236 236 9.706824 0.1920873 7.276570 0.3415444 0.02684716 0.1942983
## 237 237 9.706853 0.1920834 7.276587 0.3415864 0.02684751 0.1943318
## 238 238 9.706906 0.1920760 7.276629 0.3415859 0.02684647 0.1943426
## 239 239 9.706889 0.1920785 7.276621 0.3416035 0.02684944 0.1943714
## 240 240 9.706892 0.1920777 7.276628 0.3415893 0.02684758 0.1943579
## nvmax
## 9 9
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x9 x10 x16
## 96.70112202 -0.01426636 3.24632946 0.95963994 0.38442187 0.28876623
## x17 stat98 stat110 sqrt.x18
## 0.43713626 1.02673501 -0.96934667 7.48687040Test
if (algo.forward.caret == TRUE){
test.model(model.forward, data.test
,method = 'leapForward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 109.7 122.1 125.4 125.4 129.1 142.0
## [1] "leapForward Test MSE: 93.4589126618511"
Forward Selection with CV (w/ filtered train)
Train
if (algo.forward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "leapForward"
,feature.names = feature.names)
model.forward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 26 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 8.276677 0.1410643 6.684123 0.2155831 0.02632973 0.1530131
## 2 2 7.964853 0.2051380 6.452767 0.1909273 0.02994904 0.1500269
## 3 3 7.805053 0.2361881 6.289234 0.2298029 0.03151009 0.1643719
## 4 4 7.622695 0.2714980 6.087228 0.2226076 0.03167759 0.1714596
## 5 5 7.529243 0.2894565 6.016453 0.2136827 0.03150015 0.1652114
## 6 6 7.510020 0.2933011 6.013178 0.2178123 0.03393791 0.1754908
## 7 7 7.477940 0.2991413 5.998621 0.1977571 0.03158270 0.1635809
## 8 8 7.457692 0.3027901 5.991982 0.2007623 0.03129776 0.1654502
## 9 9 7.426062 0.3086808 5.971902 0.2009964 0.03261263 0.1703405
## 10 10 7.426107 0.3086253 5.970053 0.2029215 0.03306123 0.1751513
## 11 11 7.414367 0.3108614 5.961918 0.1922128 0.03366771 0.1725217
## 12 12 7.414979 0.3107081 5.958373 0.1988920 0.03250733 0.1713669
## 13 13 7.419061 0.3100373 5.959337 0.2045511 0.03370128 0.1767538
## 14 14 7.423084 0.3094086 5.964853 0.2060659 0.03414807 0.1763194
## 15 15 7.428243 0.3084396 5.967437 0.1992053 0.03443480 0.1704521
## 16 16 7.426650 0.3087507 5.966682 0.2045696 0.03582192 0.1762110
## 17 17 7.422114 0.3095819 5.961941 0.2019137 0.03395284 0.1724250
## 18 18 7.422310 0.3095057 5.963113 0.2035708 0.03389641 0.1727064
## 19 19 7.420272 0.3099223 5.962508 0.2030440 0.03345880 0.1713526
## 20 20 7.417713 0.3103827 5.959190 0.2007986 0.03398335 0.1699050
## 21 21 7.418911 0.3101611 5.962233 0.1954894 0.03328133 0.1692500
## 22 22 7.424423 0.3092196 5.966400 0.1961661 0.03386647 0.1684331
## 23 23 7.415982 0.3108241 5.958998 0.2030614 0.03455290 0.1762039
## 24 24 7.415269 0.3110428 5.963900 0.2051715 0.03392656 0.1797309
## 25 25 7.415506 0.3109968 5.963389 0.1953133 0.03378934 0.1770016
## 26 26 7.412032 0.3117154 5.960122 0.1955252 0.03338915 0.1765938
## 27 27 7.414963 0.3112114 5.965773 0.1977893 0.03361120 0.1795093
## 28 28 7.417700 0.3107358 5.966115 0.1971169 0.03375232 0.1780691
## 29 29 7.419764 0.3102886 5.968073 0.1954080 0.03202335 0.1761267
## 30 30 7.420295 0.3102660 5.968627 0.1968234 0.03277492 0.1799368
## 31 31 7.421289 0.3100751 5.971341 0.1967105 0.03327902 0.1821940
## 32 32 7.420444 0.3102476 5.969246 0.1984524 0.03376098 0.1799657
## 33 33 7.419642 0.3103899 5.968163 0.1940258 0.03304053 0.1775344
## 34 34 7.419467 0.3104557 5.970213 0.1986150 0.03274550 0.1805668
## 35 35 7.421622 0.3101207 5.969628 0.1968586 0.03235189 0.1790285
## 36 36 7.426997 0.3091845 5.974782 0.1960190 0.03243019 0.1792946
## 37 37 7.427208 0.3091668 5.976117 0.1954825 0.03184713 0.1771557
## 38 38 7.428055 0.3090251 5.977875 0.1938243 0.03217522 0.1750109
## 39 39 7.430736 0.3086394 5.981755 0.1949846 0.03309312 0.1742438
## 40 40 7.434463 0.3080472 5.982295 0.1968563 0.03314960 0.1751235
## 41 41 7.436495 0.3077134 5.983669 0.1952357 0.03304963 0.1733293
## 42 42 7.436842 0.3076795 5.985357 0.1947960 0.03319438 0.1756867
## 43 43 7.438201 0.3074924 5.986290 0.1976022 0.03340369 0.1772837
## 44 44 7.437004 0.3077065 5.986566 0.1963547 0.03342272 0.1766653
## 45 45 7.439847 0.3072513 5.986761 0.1946062 0.03359230 0.1726805
## 46 46 7.443452 0.3065900 5.991493 0.1950645 0.03322443 0.1738934
## 47 47 7.442533 0.3066851 5.989343 0.1936907 0.03207352 0.1702337
## 48 48 7.442738 0.3066421 5.989932 0.1919664 0.03158148 0.1715445
## 49 49 7.445685 0.3061271 5.988694 0.1930158 0.03163971 0.1743243
## 50 50 7.454101 0.3046683 5.995760 0.1956961 0.03095714 0.1740195
## 51 51 7.459767 0.3036615 6.001195 0.1947533 0.03011743 0.1723458
## 52 52 7.460244 0.3036013 6.002457 0.1944331 0.02986710 0.1732777
## 53 53 7.462601 0.3032314 6.003751 0.1958066 0.03024561 0.1767057
## 54 54 7.460366 0.3036232 6.001400 0.1987587 0.03064203 0.1807833
## 55 55 7.462090 0.3033732 6.003379 0.1982446 0.03120349 0.1816016
## 56 56 7.462891 0.3032311 6.005508 0.1964937 0.03138101 0.1787874
## 57 57 7.464554 0.3029981 6.005551 0.1947888 0.03155948 0.1749760
## 58 58 7.463472 0.3031897 6.006535 0.1946415 0.03146916 0.1748483
## 59 59 7.465664 0.3028480 6.007088 0.1954431 0.03141797 0.1758175
## 60 60 7.466879 0.3026280 6.008158 0.1953828 0.03149808 0.1766100
## 61 61 7.468635 0.3023536 6.009257 0.1952916 0.03152804 0.1783456
## 62 62 7.468503 0.3024336 6.008960 0.1947198 0.03157755 0.1786967
## 63 63 7.468219 0.3024786 6.010479 0.1933905 0.03121344 0.1771790
## 64 64 7.464739 0.3030557 6.008072 0.1920561 0.03056914 0.1753610
## 65 65 7.464879 0.3030540 6.010302 0.1968102 0.03087013 0.1789470
## 66 66 7.463636 0.3032725 6.009489 0.1954621 0.03102550 0.1780704
## 67 67 7.463269 0.3033580 6.009995 0.1932611 0.03151270 0.1770752
## 68 68 7.462434 0.3035051 6.010403 0.1907401 0.03102803 0.1742429
## 69 69 7.462886 0.3034618 6.008742 0.1906784 0.03104206 0.1725583
## 70 70 7.463123 0.3034011 6.009697 0.1876889 0.03040521 0.1694846
## 71 71 7.460532 0.3038581 6.007723 0.1840179 0.03027023 0.1670100
## 72 72 7.460338 0.3038979 6.006342 0.1818844 0.02998040 0.1658034
## 73 73 7.462140 0.3035976 6.007413 0.1825256 0.02979199 0.1667565
## 74 74 7.462729 0.3035140 6.007921 0.1827996 0.02968673 0.1659665
## 75 75 7.461762 0.3036771 6.005797 0.1795251 0.02971081 0.1617785
## 76 76 7.462023 0.3036600 6.005830 0.1775962 0.02960421 0.1603916
## 77 77 7.461291 0.3037946 6.005821 0.1778838 0.02938571 0.1613505
## 78 78 7.462275 0.3036101 6.007801 0.1756603 0.02911496 0.1606989
## 79 79 7.465156 0.3031050 6.009560 0.1766330 0.02885946 0.1602240
## 80 80 7.461502 0.3037857 6.007485 0.1770801 0.02874460 0.1611189
## 81 81 7.465990 0.3029997 6.011613 0.1773122 0.02889045 0.1614558
## 82 82 7.467410 0.3027374 6.012964 0.1768155 0.02854019 0.1602194
## 83 83 7.469210 0.3023943 6.015719 0.1781919 0.02857724 0.1614429
## 84 84 7.466844 0.3028285 6.011959 0.1769078 0.02877044 0.1633125
## 85 85 7.469427 0.3024149 6.013536 0.1740832 0.02847064 0.1602930
## 86 86 7.471292 0.3021645 6.015591 0.1739071 0.02851695 0.1600308
## 87 87 7.473260 0.3018167 6.017494 0.1732502 0.02859862 0.1596229
## 88 88 7.474822 0.3015472 6.019314 0.1729847 0.02870607 0.1592550
## 89 89 7.477386 0.3011548 6.022314 0.1713568 0.02837468 0.1587140
## 90 90 7.476260 0.3013398 6.019325 0.1706804 0.02820001 0.1591066
## 91 91 7.477408 0.3011651 6.019757 0.1673965 0.02833203 0.1559511
## 92 92 7.480093 0.3007025 6.023472 0.1656299 0.02813638 0.1541687
## 93 93 7.480903 0.3005725 6.023296 0.1638307 0.02800738 0.1523104
## 94 94 7.483120 0.3002193 6.026449 0.1658800 0.02775765 0.1530394
## 95 95 7.482205 0.3004087 6.024333 0.1636920 0.02802862 0.1534048
## 96 96 7.479981 0.3008084 6.022544 0.1645852 0.02860460 0.1534696
## 97 97 7.482969 0.3003413 6.026033 0.1655919 0.02863596 0.1547195
## 98 98 7.481007 0.3006754 6.024760 0.1652053 0.02841300 0.1527544
## 99 99 7.480439 0.3008083 6.025973 0.1663701 0.02827429 0.1535619
## 100 100 7.480128 0.3009157 6.023691 0.1696941 0.02860160 0.1570354
## 101 101 7.482445 0.3005168 6.024369 0.1730997 0.02891404 0.1597132
## 102 102 7.482838 0.3004979 6.025385 0.1745249 0.02897423 0.1599032
## 103 103 7.483100 0.3004913 6.024714 0.1734368 0.02895213 0.1595286
## 104 104 7.481947 0.3006716 6.023139 0.1743696 0.02868186 0.1596263
## 105 105 7.481811 0.3007234 6.024352 0.1742968 0.02878044 0.1598891
## 106 106 7.480959 0.3008652 6.022523 0.1763983 0.02865113 0.1604676
## 107 107 7.482765 0.3005587 6.023054 0.1764851 0.02862564 0.1614598
## 108 108 7.481605 0.3007877 6.022035 0.1778625 0.02868666 0.1625039
## 109 109 7.484069 0.3003726 6.024169 0.1770517 0.02888881 0.1615681
## 110 110 7.485097 0.3001831 6.025433 0.1795220 0.02880488 0.1623114
## 111 111 7.485451 0.3001057 6.025629 0.1772206 0.02856695 0.1607169
## 112 112 7.487026 0.2998624 6.027952 0.1764754 0.02837768 0.1592526
## 113 113 7.488442 0.2996257 6.029309 0.1783646 0.02855585 0.1595032
## 114 114 7.488235 0.2996774 6.028172 0.1785412 0.02894313 0.1579062
## 115 115 7.488713 0.2995844 6.029549 0.1787602 0.02850263 0.1593404
## 116 116 7.487469 0.2998198 6.029268 0.1808205 0.02870299 0.1599970
## 117 117 7.486327 0.3000226 6.028195 0.1808738 0.02889127 0.1616331
## 118 118 7.486276 0.3000086 6.027708 0.1833079 0.02901462 0.1638445
## 119 119 7.487338 0.2998344 6.028258 0.1854640 0.02915732 0.1646994
## 120 120 7.486394 0.3000110 6.027902 0.1824554 0.02893486 0.1618304
## 121 121 7.484009 0.3004507 6.026753 0.1831337 0.02898613 0.1614339
## 122 122 7.482997 0.3006400 6.024580 0.1826649 0.02909015 0.1625242
## 123 123 7.482171 0.3007835 6.023756 0.1800337 0.02893101 0.1605281
## 124 124 7.481071 0.3009875 6.021799 0.1803934 0.02846866 0.1613456
## 125 125 7.482232 0.3008014 6.021634 0.1814670 0.02827463 0.1625570
## 126 126 7.481502 0.3009511 6.020977 0.1815382 0.02822530 0.1630187
## 127 127 7.482014 0.3008617 6.020648 0.1817848 0.02862545 0.1626263
## 128 128 7.483585 0.3006170 6.022292 0.1811430 0.02895477 0.1616023
## 129 129 7.481480 0.3009762 6.020192 0.1797888 0.02907637 0.1605646
## 130 130 7.483460 0.3006289 6.021786 0.1790660 0.02910758 0.1605184
## 131 131 7.483107 0.3006829 6.021711 0.1795321 0.02900241 0.1609353
## 132 132 7.482092 0.3008727 6.019838 0.1787176 0.02902934 0.1606608
## 133 133 7.481158 0.3010486 6.019480 0.1788879 0.02898632 0.1618328
## 134 134 7.479812 0.3012738 6.018615 0.1782041 0.02896998 0.1617606
## 135 135 7.481181 0.3010456 6.019039 0.1789754 0.02917963 0.1622234
## 136 136 7.481527 0.3010060 6.019096 0.1795648 0.02936142 0.1630974
## 137 137 7.483376 0.3006999 6.020470 0.1807584 0.02926638 0.1634680
## 138 138 7.481984 0.3009300 6.019150 0.1810600 0.02934648 0.1636852
## 139 139 7.482675 0.3008436 6.019964 0.1813507 0.02945569 0.1640831
## 140 140 7.482824 0.3008335 6.019491 0.1825552 0.02975588 0.1651027
## 141 141 7.482279 0.3009351 6.020115 0.1823061 0.02965134 0.1650852
## 142 142 7.482474 0.3008888 6.019660 0.1821113 0.02947276 0.1649630
## 143 143 7.481860 0.3010275 6.019193 0.1812359 0.02950676 0.1642490
## 144 144 7.481294 0.3011355 6.017996 0.1814775 0.02952332 0.1633748
## 145 145 7.481548 0.3010744 6.018324 0.1822036 0.02920696 0.1632241
## 146 146 7.481776 0.3010343 6.018476 0.1809717 0.02902333 0.1620700
## 147 147 7.480346 0.3013053 6.017673 0.1793411 0.02915793 0.1609930
## 148 148 7.478763 0.3015912 6.016051 0.1786514 0.02916673 0.1597057
## 149 149 7.479040 0.3015671 6.016221 0.1793812 0.02929533 0.1602682
## 150 150 7.479327 0.3014998 6.017030 0.1794857 0.02922289 0.1609581
## 151 151 7.479041 0.3015441 6.016363 0.1793604 0.02927384 0.1608463
## 152 152 7.477661 0.3017654 6.015109 0.1804717 0.02946290 0.1618995
## 153 153 7.476982 0.3018873 6.013982 0.1802057 0.02932302 0.1616314
## 154 154 7.478503 0.3016241 6.014859 0.1821254 0.02952452 0.1628923
## 155 155 7.478509 0.3016351 6.014659 0.1829291 0.02970804 0.1631974
## 156 156 7.479887 0.3014100 6.017030 0.1825397 0.02957424 0.1630216
## 157 157 7.480425 0.3013579 6.016878 0.1831554 0.02990658 0.1632930
## 158 158 7.481245 0.3012340 6.017155 0.1832634 0.02997280 0.1633764
## 159 159 7.480333 0.3013631 6.017047 0.1845937 0.02991393 0.1651077
## 160 160 7.479888 0.3014615 6.015857 0.1846675 0.02998612 0.1644377
## 161 161 7.481210 0.3012470 6.016619 0.1844208 0.02997239 0.1643941
## 162 162 7.481237 0.3012394 6.016662 0.1843678 0.02983667 0.1637020
## 163 163 7.480427 0.3013810 6.016221 0.1837181 0.02968442 0.1636009
## 164 164 7.480962 0.3013017 6.016395 0.1835794 0.02966088 0.1639899
## 165 165 7.480850 0.3013169 6.016151 0.1840554 0.02965179 0.1633732
## 166 166 7.482862 0.3009751 6.017871 0.1850677 0.02975922 0.1634148
## 167 167 7.482645 0.3010053 6.017798 0.1848717 0.02972356 0.1626175
## 168 168 7.482692 0.3010149 6.018010 0.1845799 0.02987484 0.1626086
## 169 169 7.481354 0.3012427 6.017525 0.1841132 0.03000597 0.1611935
## 170 170 7.480918 0.3013086 6.017401 0.1833140 0.02999435 0.1606101
## 171 171 7.480083 0.3014364 6.015938 0.1828977 0.02988631 0.1603045
## 172 172 7.480094 0.3014498 6.015196 0.1832874 0.02993120 0.1600629
## 173 173 7.480534 0.3013688 6.014622 0.1819616 0.02973117 0.1593836
## 174 174 7.480489 0.3013852 6.015066 0.1821939 0.02967787 0.1598100
## 175 175 7.480764 0.3013381 6.015007 0.1817976 0.02948041 0.1596682
## 176 176 7.481243 0.3012544 6.014628 0.1813447 0.02957074 0.1594899
## 177 177 7.482678 0.3009944 6.015591 0.1811787 0.02957108 0.1598907
## 178 178 7.483913 0.3007792 6.016201 0.1816375 0.02948331 0.1599077
## 179 179 7.485003 0.3005920 6.017159 0.1807966 0.02928428 0.1602579
## 180 180 7.484789 0.3006392 6.016922 0.1817714 0.02945066 0.1612282
## 181 181 7.485704 0.3004858 6.017108 0.1823212 0.02956138 0.1615325
## 182 182 7.485430 0.3005254 6.017235 0.1832522 0.02975996 0.1620184
## 183 183 7.485078 0.3005934 6.017387 0.1838556 0.02975332 0.1627258
## 184 184 7.485577 0.3005169 6.018194 0.1825674 0.02975885 0.1612590
## 185 185 7.484987 0.3006119 6.017830 0.1827391 0.02972269 0.1617993
## 186 186 7.485088 0.3005828 6.018604 0.1826550 0.02980549 0.1617411
## 187 187 7.483924 0.3007856 6.017842 0.1822873 0.02971202 0.1612610
## 188 188 7.483933 0.3007761 6.017861 0.1824287 0.02966992 0.1613999
## 189 189 7.484617 0.3006553 6.017886 0.1831280 0.02960829 0.1616610
## 190 190 7.483764 0.3007925 6.017533 0.1831256 0.02954039 0.1614633
## 191 191 7.483680 0.3008105 6.017623 0.1832266 0.02939642 0.1619034
## 192 192 7.483806 0.3007899 6.017761 0.1820739 0.02934744 0.1608026
## 193 193 7.483795 0.3007980 6.018174 0.1815386 0.02932701 0.1603644
## 194 194 7.483603 0.3008373 6.018790 0.1815266 0.02919488 0.1609116
## 195 195 7.483214 0.3009007 6.018130 0.1815288 0.02916087 0.1607377
## 196 196 7.483225 0.3009026 6.018451 0.1812055 0.02912382 0.1605011
## 197 197 7.483340 0.3008832 6.018365 0.1812692 0.02911356 0.1610353
## 198 198 7.482784 0.3009791 6.018000 0.1819880 0.02911756 0.1611758
## 199 199 7.483268 0.3008966 6.018335 0.1813773 0.02902510 0.1609913
## 200 200 7.483485 0.3008531 6.018738 0.1811158 0.02894172 0.1610211
## 201 201 7.483710 0.3008244 6.019235 0.1812392 0.02899370 0.1610128
## 202 202 7.483452 0.3008719 6.018984 0.1815288 0.02902675 0.1608282
## 203 203 7.483470 0.3008647 6.019293 0.1814845 0.02895399 0.1610469
## 204 204 7.483601 0.3008427 6.019563 0.1815816 0.02890602 0.1615091
## 205 205 7.483740 0.3008149 6.019724 0.1818313 0.02899339 0.1616071
## 206 206 7.483539 0.3008555 6.019328 0.1821243 0.02892088 0.1616466
## 207 207 7.483902 0.3007911 6.019534 0.1822014 0.02901191 0.1614660
## 208 208 7.484087 0.3007581 6.019569 0.1823579 0.02907633 0.1616644
## 209 209 7.483420 0.3008746 6.018831 0.1826222 0.02913582 0.1618397
## 210 210 7.483889 0.3007967 6.019470 0.1827671 0.02917941 0.1619161
## 211 211 7.484136 0.3007569 6.019802 0.1823439 0.02908974 0.1615877
## 212 212 7.484250 0.3007415 6.020120 0.1825087 0.02913662 0.1617496
## 213 213 7.484230 0.3007430 6.020073 0.1821554 0.02907069 0.1615358
## 214 214 7.484500 0.3006966 6.020422 0.1824414 0.02909072 0.1615260
## 215 215 7.484778 0.3006531 6.020474 0.1824007 0.02899335 0.1615344
## 216 216 7.485185 0.3005861 6.020988 0.1819639 0.02898192 0.1613239
## 217 217 7.485561 0.3005281 6.021225 0.1819005 0.02904631 0.1612285
## 218 218 7.485676 0.3005104 6.021167 0.1817411 0.02897691 0.1611274
## 219 219 7.485282 0.3005727 6.020867 0.1816656 0.02894700 0.1613425
## 220 220 7.485534 0.3005338 6.021178 0.1818179 0.02895913 0.1613285
## 221 221 7.485310 0.3005733 6.021241 0.1816350 0.02895481 0.1611551
## 222 222 7.485330 0.3005696 6.021271 0.1815238 0.02892491 0.1613296
## 223 223 7.485533 0.3005342 6.021232 0.1815954 0.02897600 0.1614133
## 224 224 7.485393 0.3005600 6.021279 0.1816679 0.02897485 0.1615359
## 225 225 7.485507 0.3005400 6.021474 0.1814428 0.02895719 0.1614197
## 226 226 7.485774 0.3004966 6.021803 0.1813580 0.02901401 0.1612259
## 227 227 7.485958 0.3004613 6.022085 0.1812933 0.02899000 0.1609602
## 228 228 7.485903 0.3004686 6.022126 0.1813543 0.02896481 0.1611853
## 229 229 7.485865 0.3004746 6.022094 0.1813131 0.02895796 0.1611545
## 230 230 7.485790 0.3004865 6.022059 0.1810982 0.02895731 0.1609416
## 231 231 7.485832 0.3004797 6.021987 0.1811091 0.02894470 0.1608509
## 232 232 7.485895 0.3004679 6.022014 0.1811359 0.02891979 0.1608564
## 233 233 7.485961 0.3004561 6.022020 0.1813644 0.02891960 0.1609937
## 234 234 7.486026 0.3004439 6.022105 0.1813039 0.02891242 0.1609771
## 235 235 7.486028 0.3004447 6.022125 0.1813252 0.02891942 0.1610493
## 236 236 7.486036 0.3004439 6.022118 0.1812701 0.02891840 0.1610519
## 237 237 7.486065 0.3004386 6.022129 0.1812642 0.02891534 0.1610839
## 238 238 7.486033 0.3004442 6.022107 0.1812514 0.02891806 0.1610742
## 239 239 7.486022 0.3004460 6.022095 0.1812484 0.02891771 0.1610637
## 240 240 7.486026 0.3004453 6.022099 0.1812374 0.02891745 0.1610546
## nvmax
## 26 26
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x8 x9
## 8.779440e+01 -1.557120e-02 3.403876e+00 1.539538e-01 9.284070e-01
## x10 x11 x16 x17 x21
## 4.688309e-01 5.283539e+07 2.962353e-01 4.203017e-01 2.574440e-02
## stat4 stat6 stat14 stat23 stat24
## -1.596461e-01 -1.395450e-01 -2.564100e-01 1.449456e-01 -1.415226e-01
## stat33 stat35 stat38 stat41 stat98
## -1.700816e-01 -1.421244e-01 1.890886e-01 -1.606934e-01 9.455994e-01
## stat100 stat110 stat144 stat149 stat156
## 1.878400e-01 -8.985658e-01 1.437621e-01 -1.939056e-01 2.086850e-01
## stat172 sqrt.x18
## 1.666971e-01 7.327670e+00Test
if (algo.forward.caret == TRUE){
test.model(model.forward, data.test
,method = 'leapForward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 106.4 120.8 124.2 124.2 127.9 138.9
## [1] "leapForward Test MSE: 94.1712443109364"
Backward Elimination
Train
if (algo.backward == TRUE){
# Takes too much time
t1 = Sys.time()
model.backward = step(model.full, data = data.train, direction="backward", trace = 0)
print(summary(model.backward))
t2 = Sys.time()
print (paste("Time taken for Backward Elimination: ",t2-t1, sep = ""))
plot.diagnostics(model.backward, data.train)
}Test
if (algo.backward == TRUE){
test.model(model.backard, data.test, "Backward Elimination")
}Backward Elimination with CV (w/ full train)
Train
if (algo.backward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "leapBackward"
,feature.names = feature.names)
model.backward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 9 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 10.210693 0.1018765 7.806589 0.3486064 0.02595312 0.1962709
## 2 2 9.979515 0.1418363 7.597398 0.3207096 0.02875352 0.1777050
## 3 3 9.814897 0.1693658 7.438338 0.3290643 0.02785753 0.1807549
## 4 4 9.667493 0.1938902 7.231990 0.3201540 0.02682717 0.1803033
## 5 5 9.588875 0.2068466 7.172167 0.3419604 0.02484619 0.1975346
## 6 6 9.591660 0.2066208 7.174795 0.3521685 0.02739708 0.2020679
## 7 7 9.571737 0.2099116 7.168707 0.3525200 0.02833739 0.2076171
## 8 8 9.546348 0.2140867 7.150908 0.3571826 0.02810121 0.2058362
## 9 9 9.521497 0.2180754 7.135342 0.3515932 0.02753943 0.1919503
## 10 10 9.531634 0.2164981 7.142145 0.3502059 0.02798899 0.1947027
## 11 11 9.531499 0.2165176 7.142129 0.3493156 0.02735269 0.1916070
## 12 12 9.541275 0.2149978 7.148145 0.3511383 0.02743081 0.1879036
## 13 13 9.539867 0.2151993 7.145155 0.3456114 0.02644858 0.1873780
## 14 14 9.532611 0.2163848 7.137358 0.3470188 0.02761253 0.1915646
## 15 15 9.528434 0.2170681 7.130122 0.3501793 0.02790495 0.1974392
## 16 16 9.533248 0.2163267 7.130545 0.3528825 0.02850968 0.1979491
## 17 17 9.534851 0.2160552 7.129357 0.3505236 0.02748267 0.1945657
## 18 18 9.536197 0.2158903 7.134786 0.3468297 0.02764942 0.1942370
## 19 19 9.536516 0.2158327 7.137370 0.3465569 0.02819126 0.1926959
## 20 20 9.537824 0.2156687 7.135493 0.3478763 0.02871909 0.1898675
## 21 21 9.536678 0.2159513 7.131799 0.3583694 0.02959617 0.1937490
## 22 22 9.541966 0.2150980 7.135841 0.3539049 0.02907704 0.1940634
## 23 23 9.545257 0.2145512 7.138423 0.3525134 0.02890386 0.1901105
## 24 24 9.552337 0.2134013 7.144349 0.3532654 0.02854211 0.1900327
## 25 25 9.559340 0.2124073 7.153893 0.3595815 0.02969110 0.1953156
## 26 26 9.564942 0.2114551 7.160867 0.3533027 0.02865346 0.1898825
## 27 27 9.567153 0.2111656 7.162236 0.3551718 0.02879216 0.1906402
## 28 28 9.578157 0.2094785 7.165991 0.3565853 0.02874149 0.1899061
## 29 29 9.585088 0.2084604 7.168349 0.3602446 0.02912832 0.1918108
## 30 30 9.591335 0.2075436 7.171271 0.3663210 0.02935303 0.1971488
## 31 31 9.598155 0.2065258 7.178620 0.3631845 0.02895114 0.1969082
## 32 32 9.601486 0.2060684 7.183593 0.3675440 0.02949985 0.2051137
## 33 33 9.604653 0.2055913 7.185761 0.3634878 0.02906543 0.2051718
## 34 34 9.609242 0.2049042 7.187453 0.3652655 0.02890848 0.2104810
## 35 35 9.611277 0.2045781 7.190543 0.3586124 0.02837577 0.2091363
## 36 36 9.617242 0.2037266 7.197623 0.3591314 0.02839541 0.2085561
## 37 37 9.618621 0.2035180 7.199299 0.3572157 0.02780645 0.2103565
## 38 38 9.623021 0.2028526 7.199195 0.3576192 0.02780702 0.2100995
## 39 39 9.627222 0.2022288 7.202209 0.3583861 0.02814230 0.2132511
## 40 40 9.628373 0.2020733 7.205311 0.3583978 0.02777717 0.2127988
## 41 41 9.633754 0.2012107 7.211548 0.3573159 0.02727663 0.2123332
## 42 42 9.637859 0.2005687 7.215515 0.3590490 0.02730294 0.2129728
## 43 43 9.643917 0.1996767 7.222107 0.3574981 0.02702196 0.2098385
## 44 44 9.647104 0.1992357 7.223696 0.3604460 0.02746196 0.2119928
## 45 45 9.646832 0.1993935 7.224951 0.3618233 0.02810861 0.2159876
## 46 46 9.650030 0.1988921 7.225393 0.3591478 0.02801080 0.2156596
## 47 47 9.650792 0.1988018 7.224751 0.3558083 0.02807442 0.2112062
## 48 48 9.652924 0.1985198 7.227251 0.3568715 0.02854845 0.2141574
## 49 49 9.658965 0.1976209 7.230449 0.3559228 0.02817482 0.2151760
## 50 50 9.663520 0.1969755 7.236301 0.3582745 0.02841885 0.2188495
## 51 51 9.665446 0.1966864 7.238810 0.3582130 0.02798934 0.2160960
## 52 52 9.667487 0.1963912 7.239920 0.3564635 0.02800182 0.2119510
## 53 53 9.668056 0.1963089 7.241985 0.3598707 0.02801428 0.2153900
## 54 54 9.670989 0.1959324 7.245157 0.3591777 0.02773111 0.2130229
## 55 55 9.675101 0.1953436 7.249609 0.3577183 0.02739725 0.2110367
## 56 56 9.676830 0.1951037 7.250394 0.3623240 0.02744127 0.2139670
## 57 57 9.679797 0.1946754 7.249255 0.3615492 0.02742856 0.2143840
## 58 58 9.679046 0.1947743 7.251771 0.3603634 0.02734686 0.2122990
## 59 59 9.676757 0.1951176 7.251144 0.3616330 0.02751501 0.2163119
## 60 60 9.677988 0.1949203 7.248823 0.3591144 0.02738875 0.2136221
## 61 61 9.682974 0.1941887 7.255781 0.3568176 0.02741414 0.2108565
## 62 62 9.681945 0.1943356 7.256227 0.3553458 0.02745293 0.2088039
## 63 63 9.684731 0.1939745 7.256336 0.3563263 0.02747387 0.2084639
## 64 64 9.686669 0.1937188 7.256965 0.3567159 0.02762404 0.2105528
## 65 65 9.690756 0.1931640 7.259871 0.3593361 0.02760911 0.2143642
## 66 66 9.693208 0.1928502 7.263137 0.3569962 0.02749936 0.2144834
## 67 67 9.691543 0.1931161 7.260283 0.3565026 0.02721116 0.2143375
## 68 68 9.690978 0.1932330 7.259098 0.3566041 0.02728468 0.2118798
## 69 69 9.688821 0.1935501 7.257127 0.3576551 0.02745005 0.2115901
## 70 70 9.684935 0.1941809 7.253396 0.3592030 0.02775692 0.2102125
## 71 71 9.687518 0.1937960 7.256291 0.3539772 0.02713243 0.2037136
## 72 72 9.687527 0.1937583 7.255942 0.3527590 0.02663387 0.2048789
## 73 73 9.686302 0.1939830 7.257381 0.3523154 0.02660123 0.2024226
## 74 74 9.685768 0.1940980 7.254324 0.3498765 0.02653866 0.2015641
## 75 75 9.688213 0.1937263 7.255980 0.3471208 0.02657223 0.1974896
## 76 76 9.687497 0.1938721 7.255461 0.3487272 0.02669801 0.1979915
## 77 77 9.690580 0.1934465 7.257197 0.3469125 0.02691485 0.1960903
## 78 78 9.691207 0.1933821 7.257562 0.3473227 0.02669748 0.1964870
## 79 79 9.691675 0.1933438 7.258956 0.3472902 0.02641815 0.1968501
## 80 80 9.693854 0.1930749 7.261112 0.3508407 0.02704887 0.1976880
## 81 81 9.692890 0.1932319 7.260472 0.3524775 0.02739940 0.1985055
## 82 82 9.692245 0.1933068 7.261453 0.3511637 0.02684694 0.1992143
## 83 83 9.693466 0.1931490 7.262818 0.3504703 0.02650340 0.2003574
## 84 84 9.694028 0.1931134 7.263768 0.3507373 0.02677541 0.2009263
## 85 85 9.694468 0.1930435 7.264639 0.3495535 0.02668432 0.2007437
## 86 86 9.694891 0.1930021 7.264524 0.3488007 0.02657575 0.1999154
## 87 87 9.693891 0.1931946 7.262981 0.3539819 0.02683954 0.2072625
## 88 88 9.695446 0.1929828 7.265615 0.3535711 0.02666659 0.2058616
## 89 89 9.693228 0.1933061 7.264339 0.3500042 0.02649108 0.2019296
## 90 90 9.694645 0.1930854 7.265619 0.3484994 0.02670515 0.2009905
## 91 91 9.693245 0.1933070 7.265675 0.3511601 0.02724310 0.2028728
## 92 92 9.694332 0.1931339 7.267006 0.3483889 0.02696075 0.2022797
## 93 93 9.695279 0.1930057 7.268581 0.3490137 0.02692229 0.2021526
## 94 94 9.694399 0.1931330 7.268371 0.3509579 0.02687495 0.2017675
## 95 95 9.696027 0.1928964 7.267307 0.3503583 0.02674838 0.2022347
## 96 96 9.695892 0.1928995 7.267799 0.3466564 0.02677999 0.2001109
## 97 97 9.694797 0.1931080 7.265956 0.3466219 0.02693330 0.1994421
## 98 98 9.692698 0.1934263 7.264149 0.3479106 0.02733316 0.2008395
## 99 99 9.693191 0.1933653 7.266731 0.3459556 0.02708994 0.2000581
## 100 100 9.694230 0.1931909 7.267642 0.3453334 0.02695419 0.2006398
## 101 101 9.694665 0.1931135 7.269663 0.3463024 0.02671130 0.2018744
## 102 102 9.696332 0.1928894 7.270051 0.3463249 0.02700542 0.2009760
## 103 103 9.693765 0.1932943 7.267977 0.3479165 0.02699315 0.2023682
## 104 104 9.693350 0.1933374 7.267306 0.3461054 0.02681763 0.2001660
## 105 105 9.694099 0.1932599 7.265960 0.3464355 0.02679231 0.2003628
## 106 106 9.694248 0.1932680 7.264014 0.3495186 0.02704639 0.2035439
## 107 107 9.696082 0.1930190 7.263139 0.3487028 0.02680455 0.2043117
## 108 108 9.697494 0.1928198 7.264114 0.3472730 0.02663206 0.2036389
## 109 109 9.697918 0.1928037 7.264428 0.3465157 0.02637458 0.2035872
## 110 110 9.697535 0.1928892 7.263764 0.3465883 0.02648241 0.2018739
## 111 111 9.699857 0.1925462 7.265406 0.3444297 0.02631810 0.2001005
## 112 112 9.700671 0.1924654 7.266938 0.3425239 0.02628710 0.1999002
## 113 113 9.699430 0.1926423 7.267804 0.3407401 0.02613954 0.2001406
## 114 114 9.700733 0.1924807 7.268004 0.3424886 0.02614898 0.2015903
## 115 115 9.701195 0.1924579 7.268317 0.3418141 0.02638172 0.2002767
## 116 116 9.701859 0.1923904 7.267324 0.3421685 0.02631705 0.1999053
## 117 117 9.703508 0.1921210 7.268201 0.3392631 0.02624918 0.1967074
## 118 118 9.702222 0.1922814 7.266561 0.3379846 0.02607425 0.1955241
## 119 119 9.701533 0.1923890 7.266576 0.3383208 0.02610860 0.1979635
## 120 120 9.699171 0.1927467 7.267153 0.3383421 0.02625200 0.1986509
## 121 121 9.700917 0.1925431 7.267551 0.3398877 0.02635284 0.1980885
## 122 122 9.699888 0.1926985 7.267375 0.3390506 0.02639720 0.1973481
## 123 123 9.699886 0.1927058 7.267415 0.3388495 0.02651560 0.1963265
## 124 124 9.700670 0.1925790 7.268024 0.3375058 0.02619287 0.1936089
## 125 125 9.700155 0.1926996 7.268723 0.3380325 0.02606751 0.1940960
## 126 126 9.699130 0.1928734 7.268690 0.3374778 0.02625507 0.1936276
## 127 127 9.700602 0.1926540 7.270243 0.3362610 0.02614090 0.1931906
## 128 128 9.701390 0.1925252 7.271134 0.3360868 0.02625250 0.1925629
## 129 129 9.701509 0.1925126 7.271675 0.3363617 0.02629810 0.1926265
## 130 130 9.701786 0.1925010 7.271268 0.3365147 0.02632620 0.1929906
## 131 131 9.700856 0.1926622 7.271408 0.3370343 0.02648114 0.1942717
## 132 132 9.701056 0.1926271 7.271316 0.3365443 0.02617507 0.1926948
## 133 133 9.701189 0.1926198 7.270559 0.3383092 0.02625815 0.1946690
## 134 134 9.699918 0.1928212 7.270029 0.3389727 0.02628794 0.1947150
## 135 135 9.700284 0.1927719 7.271585 0.3387967 0.02622649 0.1959216
## 136 136 9.700587 0.1927551 7.271776 0.3382973 0.02627889 0.1955920
## 137 137 9.700030 0.1928449 7.271412 0.3391261 0.02664764 0.1956274
## 138 138 9.701284 0.1926953 7.272473 0.3398064 0.02659261 0.1967699
## 139 139 9.702943 0.1924654 7.273342 0.3383317 0.02652047 0.1949653
## 140 140 9.702719 0.1924891 7.273287 0.3383299 0.02671578 0.1942982
## 141 141 9.701634 0.1926481 7.272199 0.3363367 0.02656415 0.1925802
## 142 142 9.702920 0.1924607 7.273210 0.3357138 0.02644660 0.1913421
## 143 143 9.704954 0.1921832 7.275617 0.3357353 0.02659447 0.1905552
## 144 144 9.704304 0.1922734 7.275483 0.3359528 0.02653808 0.1893606
## 145 145 9.704598 0.1922769 7.276437 0.3357202 0.02671569 0.1887101
## 146 146 9.706156 0.1920346 7.277584 0.3367335 0.02683361 0.1901965
## 147 147 9.705219 0.1921676 7.276732 0.3368533 0.02676368 0.1901619
## 148 148 9.706015 0.1920676 7.277644 0.3359080 0.02682262 0.1905669
## 149 149 9.707198 0.1919343 7.278786 0.3376662 0.02713818 0.1912218
## 150 150 9.706231 0.1920750 7.277242 0.3367319 0.02705782 0.1891128
## 151 151 9.706870 0.1919910 7.277071 0.3382122 0.02706506 0.1892865
## 152 152 9.705992 0.1921238 7.276278 0.3383902 0.02701045 0.1900168
## 153 153 9.704477 0.1923266 7.275587 0.3382009 0.02706117 0.1907077
## 154 154 9.704781 0.1922846 7.276010 0.3380793 0.02696273 0.1904201
## 155 155 9.704316 0.1923475 7.275390 0.3380834 0.02686155 0.1910122
## 156 156 9.703228 0.1925016 7.273727 0.3383367 0.02675757 0.1896602
## 157 157 9.703627 0.1924222 7.273189 0.3382846 0.02674206 0.1902202
## 158 158 9.703481 0.1924357 7.273005 0.3390063 0.02666209 0.1908757
## 159 159 9.704909 0.1922154 7.274037 0.3393491 0.02674079 0.1902913
## 160 160 9.703127 0.1924782 7.271705 0.3401949 0.02673237 0.1908518
## 161 161 9.702727 0.1925302 7.271220 0.3386306 0.02661123 0.1891912
## 162 162 9.702753 0.1925473 7.271521 0.3395543 0.02675071 0.1903630
## 163 163 9.701532 0.1927338 7.270820 0.3410812 0.02700245 0.1905563
## 164 164 9.701696 0.1927149 7.271155 0.3414445 0.02685013 0.1909318
## 165 165 9.702190 0.1926441 7.272242 0.3417180 0.02668801 0.1908188
## 166 166 9.702391 0.1926209 7.272864 0.3412582 0.02663495 0.1899455
## 167 167 9.702464 0.1926121 7.272514 0.3411960 0.02674623 0.1892967
## 168 168 9.702612 0.1926056 7.272601 0.3421849 0.02706887 0.1892675
## 169 169 9.702930 0.1925533 7.272853 0.3420169 0.02695621 0.1891021
## 170 170 9.702514 0.1926427 7.272702 0.3442184 0.02721634 0.1924847
## 171 171 9.702272 0.1926780 7.272644 0.3431826 0.02721253 0.1921730
## 172 172 9.701911 0.1927534 7.272337 0.3444367 0.02733291 0.1932607
## 173 173 9.701632 0.1927780 7.272172 0.3444195 0.02730727 0.1940425
## 174 174 9.700646 0.1929184 7.271452 0.3432577 0.02732081 0.1930826
## 175 175 9.700944 0.1928960 7.271902 0.3432280 0.02742227 0.1932193
## 176 176 9.701463 0.1928216 7.271954 0.3426500 0.02725610 0.1925960
## 177 177 9.702926 0.1926085 7.272968 0.3432448 0.02711976 0.1933876
## 178 178 9.703376 0.1925637 7.273522 0.3456493 0.02738826 0.1955659
## 179 179 9.703464 0.1925618 7.273980 0.3462369 0.02728684 0.1952728
## 180 180 9.703607 0.1925353 7.274232 0.3461024 0.02721610 0.1960882
## 181 181 9.703679 0.1925261 7.274611 0.3460015 0.02729047 0.1962450
## 182 182 9.704920 0.1923432 7.275517 0.3465744 0.02750705 0.1967721
## 183 183 9.704172 0.1924545 7.275013 0.3464381 0.02752247 0.1955989
## 184 184 9.704787 0.1923746 7.275449 0.3458979 0.02752517 0.1953603
## 185 185 9.705034 0.1923464 7.275480 0.3460214 0.02747340 0.1950640
## 186 186 9.705830 0.1922384 7.275870 0.3455084 0.02736277 0.1955410
## 187 187 9.704883 0.1923773 7.275090 0.3450902 0.02726262 0.1957395
## 188 188 9.705612 0.1922647 7.275936 0.3448850 0.02722264 0.1954333
## 189 189 9.705123 0.1923216 7.274658 0.3443437 0.02721938 0.1955785
## 190 190 9.706257 0.1921484 7.275502 0.3438829 0.02718141 0.1947806
## 191 191 9.706458 0.1921066 7.275537 0.3430451 0.02688195 0.1948499
## 192 192 9.706955 0.1920259 7.276070 0.3432975 0.02686359 0.1951445
## 193 193 9.707689 0.1919251 7.276148 0.3432987 0.02686485 0.1947790
## 194 194 9.707287 0.1919870 7.276099 0.3435543 0.02689076 0.1950857
## 195 195 9.707731 0.1919316 7.276503 0.3430502 0.02685739 0.1948084
## 196 196 9.706798 0.1920676 7.276143 0.3427109 0.02693133 0.1941550
## 197 197 9.706856 0.1920773 7.275998 0.3433947 0.02698426 0.1947861
## 198 198 9.706930 0.1920668 7.275930 0.3439761 0.02702817 0.1951985
## 199 199 9.706706 0.1920892 7.276041 0.3433670 0.02691884 0.1943902
## 200 200 9.706955 0.1920651 7.276634 0.3436436 0.02698959 0.1950841
## 201 201 9.706813 0.1920812 7.276180 0.3431026 0.02697407 0.1948717
## 202 202 9.707248 0.1920176 7.276412 0.3426836 0.02694603 0.1949861
## 203 203 9.706840 0.1920819 7.276036 0.3427547 0.02698682 0.1947028
## 204 204 9.706945 0.1920697 7.276208 0.3418236 0.02690521 0.1944195
## 205 205 9.707119 0.1920516 7.276126 0.3422049 0.02686679 0.1948274
## 206 206 9.707184 0.1920361 7.276479 0.3417104 0.02686366 0.1949266
## 207 207 9.707510 0.1919813 7.277028 0.3414082 0.02681033 0.1946199
## 208 208 9.707079 0.1920474 7.276749 0.3423171 0.02687667 0.1953371
## 209 209 9.707098 0.1920555 7.276526 0.3423560 0.02689591 0.1952065
## 210 210 9.706889 0.1920889 7.276527 0.3423302 0.02688841 0.1946550
## 211 211 9.707230 0.1920369 7.276482 0.3420154 0.02689284 0.1944229
## 212 212 9.707232 0.1920256 7.276565 0.3417825 0.02685283 0.1942663
## 213 213 9.707529 0.1919811 7.276811 0.3422276 0.02685810 0.1946583
## 214 214 9.706872 0.1920779 7.276312 0.3421563 0.02687518 0.1946258
## 215 215 9.707125 0.1920436 7.276537 0.3423709 0.02688269 0.1948166
## 216 216 9.707523 0.1919918 7.276899 0.3421291 0.02683683 0.1943649
## 217 217 9.707587 0.1919836 7.276913 0.3417189 0.02684910 0.1941020
## 218 218 9.707312 0.1920244 7.276848 0.3418386 0.02686413 0.1943437
## 219 219 9.707181 0.1920418 7.276845 0.3416553 0.02685859 0.1940363
## 220 220 9.707422 0.1920032 7.277065 0.3416047 0.02686831 0.1939818
## 221 221 9.707501 0.1919944 7.277178 0.3418774 0.02689742 0.1940936
## 222 222 9.707310 0.1920203 7.276914 0.3417660 0.02687880 0.1941040
## 223 223 9.707131 0.1920444 7.276929 0.3416600 0.02687449 0.1941464
## 224 224 9.707377 0.1920082 7.277091 0.3416442 0.02686254 0.1942232
## 225 225 9.707262 0.1920221 7.276725 0.3415707 0.02684678 0.1942614
## 226 226 9.707045 0.1920539 7.276607 0.3416431 0.02685931 0.1943162
## 227 227 9.706838 0.1920822 7.276457 0.3414971 0.02684527 0.1940610
## 228 228 9.706890 0.1920750 7.276668 0.3413004 0.02681223 0.1939648
## 229 229 9.706862 0.1920806 7.276604 0.3413612 0.02683954 0.1941413
## 230 230 9.706910 0.1920736 7.276613 0.3413077 0.02683709 0.1940985
## 231 231 9.706816 0.1920869 7.276530 0.3414668 0.02684573 0.1942758
## 232 232 9.706798 0.1920900 7.276554 0.3414690 0.02683953 0.1942318
## 233 233 9.706781 0.1920922 7.276498 0.3415837 0.02685977 0.1943116
## 234 234 9.706862 0.1920808 7.276539 0.3414934 0.02684693 0.1942315
## 235 235 9.706860 0.1920807 7.276577 0.3414986 0.02684323 0.1942913
## 236 236 9.706824 0.1920873 7.276570 0.3415444 0.02684716 0.1942983
## 237 237 9.706853 0.1920834 7.276587 0.3415864 0.02684751 0.1943318
## 238 238 9.706906 0.1920760 7.276629 0.3415859 0.02684647 0.1943426
## 239 239 9.706889 0.1920785 7.276621 0.3416035 0.02684944 0.1943714
## 240 240 9.706892 0.1920777 7.276628 0.3415893 0.02684758 0.1943579
## nvmax
## 9 9
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x9 x10 x16
## 96.70112202 -0.01426636 3.24632946 0.95963994 0.38442187 0.28876623
## x17 stat98 stat110 sqrt.x18
## 0.43713626 1.02673501 -0.96934667 7.48687040Test
if (algo.backward.caret == TRUE){
test.model(model.backward, data.test
,method = 'leapBackward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 109.7 122.1 125.4 125.4 129.1 142.0
## [1] "leapBackward Test MSE: 93.4589126618511"
Backward Elimination with CV (w/ filtered train)
Train
if (algo.backward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "leapBackward"
,feature.names = feature.names)
model.backward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 26 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 8.276677 0.1410643 6.684123 0.2155831 0.02632973 0.1530131
## 2 2 7.964853 0.2051380 6.452767 0.1909273 0.02994904 0.1500269
## 3 3 7.805053 0.2361881 6.289234 0.2298029 0.03151009 0.1643719
## 4 4 7.622695 0.2714980 6.087228 0.2226076 0.03167759 0.1714596
## 5 5 7.529243 0.2894565 6.016453 0.2136827 0.03150015 0.1652114
## 6 6 7.510020 0.2933011 6.013178 0.2178123 0.03393791 0.1754908
## 7 7 7.477940 0.2991413 5.998621 0.1977571 0.03158270 0.1635809
## 8 8 7.457692 0.3027901 5.991982 0.2007623 0.03129776 0.1654502
## 9 9 7.426062 0.3086808 5.971902 0.2009964 0.03261263 0.1703405
## 10 10 7.426107 0.3086253 5.970053 0.2029215 0.03306123 0.1751513
## 11 11 7.414367 0.3108614 5.961918 0.1922128 0.03366771 0.1725217
## 12 12 7.414979 0.3107081 5.958373 0.1988920 0.03250733 0.1713669
## 13 13 7.419061 0.3100373 5.959337 0.2045511 0.03370128 0.1767538
## 14 14 7.423021 0.3093897 5.964115 0.2061276 0.03416069 0.1770958
## 15 15 7.426975 0.3086460 5.964136 0.2005644 0.03431245 0.1742788
## 16 16 7.423712 0.3092999 5.963404 0.2059067 0.03562611 0.1774693
## 17 17 7.421415 0.3097418 5.961632 0.2009623 0.03462736 0.1699046
## 18 18 7.425364 0.3089498 5.964703 0.1965068 0.03381535 0.1668292
## 19 19 7.426429 0.3087858 5.966779 0.1942314 0.03253504 0.1641963
## 20 20 7.420735 0.3097839 5.962694 0.1904969 0.03268285 0.1655002
## 21 21 7.417451 0.3104281 5.958636 0.1940692 0.03319040 0.1685503
## 22 22 7.417843 0.3104054 5.958917 0.1996032 0.03316233 0.1734208
## 23 23 7.411809 0.3116230 5.954806 0.2066194 0.03419916 0.1807268
## 24 24 7.416370 0.3108508 5.962561 0.2039085 0.03401727 0.1814145
## 25 25 7.417251 0.3106764 5.964876 0.1962715 0.03330188 0.1776935
## 26 26 7.411350 0.3118360 5.960283 0.1951685 0.03357305 0.1755140
## 27 27 7.414032 0.3113712 5.964218 0.1988576 0.03352832 0.1813936
## 28 28 7.417734 0.3107293 5.965755 0.1970509 0.03374472 0.1787530
## 29 29 7.419768 0.3102819 5.967800 0.1954006 0.03201553 0.1766429
## 30 30 7.419563 0.3103889 5.966026 0.1990140 0.03271711 0.1793984
## 31 31 7.422214 0.3099124 5.971182 0.2002072 0.03348862 0.1828807
## 32 32 7.420444 0.3102476 5.969246 0.1984524 0.03376098 0.1799657
## 33 33 7.419446 0.3104476 5.967172 0.1952250 0.03308624 0.1773729
## 34 34 7.420581 0.3102763 5.967490 0.1991398 0.03301749 0.1813087
## 35 35 7.422748 0.3099213 5.970411 0.1972287 0.03261898 0.1795422
## 36 36 7.427236 0.3091648 5.975035 0.1957071 0.03252185 0.1781542
## 37 37 7.424921 0.3096022 5.974007 0.1939252 0.03228963 0.1749930
## 38 38 7.425013 0.3096528 5.973786 0.1927712 0.03233700 0.1751939
## 39 39 7.429927 0.3088347 5.978202 0.1949028 0.03288434 0.1762552
## 40 40 7.433224 0.3083147 5.980247 0.1966553 0.03278040 0.1777586
## 41 41 7.435000 0.3080562 5.983696 0.1956602 0.03345355 0.1747626
## 42 42 7.437596 0.3075953 5.984304 0.1952054 0.03326415 0.1751041
## 43 43 7.437450 0.3076086 5.985777 0.1971093 0.03332401 0.1769541
## 44 44 7.439724 0.3071832 5.988489 0.1973279 0.03249548 0.1772975
## 45 45 7.444587 0.3062874 5.991143 0.1929155 0.03202202 0.1722841
## 46 46 7.446435 0.3059818 5.993523 0.1923556 0.03230585 0.1714371
## 47 47 7.445088 0.3061662 5.990944 0.1907561 0.03119105 0.1671542
## 48 48 7.444733 0.3062917 5.992794 0.1921053 0.03110801 0.1703214
## 49 49 7.447215 0.3059060 5.992315 0.1923540 0.03179091 0.1720461
## 50 50 7.454515 0.3046378 5.997594 0.1954035 0.03100057 0.1717813
## 51 51 7.459043 0.3038129 6.000984 0.1959493 0.03043160 0.1714843
## 52 52 7.457689 0.3040567 5.999973 0.1950412 0.02999058 0.1732763
## 53 53 7.460161 0.3036438 6.002046 0.1941133 0.02996394 0.1749587
## 54 54 7.458665 0.3039021 5.998856 0.1957932 0.03018665 0.1765836
## 55 55 7.458525 0.3039893 5.998818 0.1946610 0.03073146 0.1765882
## 56 56 7.458495 0.3040066 6.000316 0.1925586 0.03111565 0.1750025
## 57 57 7.461618 0.3034921 6.002628 0.1908995 0.03107889 0.1734092
## 58 58 7.461668 0.3035251 6.003637 0.1910577 0.03114920 0.1736469
## 59 59 7.462687 0.3033887 6.002845 0.1938016 0.03126947 0.1743759
## 60 60 7.464981 0.3029931 6.004503 0.1971868 0.03167026 0.1765744
## 61 61 7.464731 0.3030484 6.004223 0.1954055 0.03138997 0.1764833
## 62 62 7.467226 0.3026794 6.006139 0.1944846 0.03146926 0.1769925
## 63 63 7.466802 0.3027368 6.008022 0.1943179 0.03108656 0.1753396
## 64 64 7.465766 0.3028847 6.007578 0.1936747 0.03099566 0.1753070
## 65 65 7.466286 0.3028429 6.009350 0.1970834 0.03117439 0.1789336
## 66 66 7.465028 0.3030599 6.008531 0.1949815 0.03119748 0.1779753
## 67 67 7.464328 0.3031795 6.010289 0.1926638 0.03097459 0.1755227
## 68 68 7.462007 0.3035653 6.008786 0.1854068 0.03008338 0.1699636
## 69 69 7.462793 0.3034408 6.008969 0.1835349 0.02976806 0.1672234
## 70 70 7.462629 0.3034568 6.006997 0.1797423 0.02935917 0.1634003
## 71 71 7.462134 0.3035717 6.007213 0.1799822 0.02985786 0.1621585
## 72 72 7.462247 0.3035738 6.005950 0.1799824 0.02957842 0.1620222
## 73 73 7.463903 0.3032994 6.005818 0.1797476 0.02890039 0.1615549
## 74 74 7.465092 0.3031077 6.006152 0.1816170 0.02937457 0.1630813
## 75 75 7.463993 0.3033324 6.005917 0.1784448 0.02935526 0.1594180
## 76 76 7.463569 0.3034421 6.007364 0.1774970 0.02936867 0.1588056
## 77 77 7.462535 0.3036206 6.006788 0.1762982 0.02906282 0.1583425
## 78 78 7.462935 0.3035315 6.006244 0.1737701 0.02888051 0.1567091
## 79 79 7.464049 0.3033087 6.008205 0.1748559 0.02874236 0.1580091
## 80 80 7.463758 0.3033579 6.008287 0.1770595 0.02830228 0.1612032
## 81 81 7.463490 0.3034136 6.007967 0.1757702 0.02853463 0.1604572
## 82 82 7.463714 0.3033760 6.009262 0.1747184 0.02833203 0.1584868
## 83 83 7.463469 0.3034136 6.010350 0.1742840 0.02857277 0.1582699
## 84 84 7.465807 0.3030160 6.011713 0.1760806 0.02881884 0.1622793
## 85 85 7.467805 0.3026852 6.012519 0.1762462 0.02877271 0.1624106
## 86 86 7.470393 0.3022776 6.015897 0.1743797 0.02899617 0.1609173
## 87 87 7.472144 0.3019899 6.016538 0.1746394 0.02888909 0.1622009
## 88 88 7.476761 0.3011887 6.020658 0.1751178 0.02875329 0.1645592
## 89 89 7.476717 0.3012118 6.020685 0.1728937 0.02852949 0.1617514
## 90 90 7.478548 0.3008816 6.021269 0.1712955 0.02819678 0.1616327
## 91 91 7.480706 0.3005357 6.023770 0.1685096 0.02837716 0.1577960
## 92 92 7.481694 0.3003920 6.023754 0.1684273 0.02843920 0.1580735
## 93 93 7.480452 0.3006354 6.020983 0.1674590 0.02843810 0.1571993
## 94 94 7.481129 0.3005364 6.021736 0.1659882 0.02789137 0.1550889
## 95 95 7.477560 0.3011886 6.019449 0.1658501 0.02760519 0.1544966
## 96 96 7.476061 0.3014778 6.019244 0.1657852 0.02813452 0.1539826
## 97 97 7.477624 0.3012274 6.020994 0.1667465 0.02793112 0.1546537
## 98 98 7.478794 0.3010609 6.022099 0.1687038 0.02805873 0.1572810
## 99 99 7.478852 0.3010852 6.021116 0.1695024 0.02814192 0.1591237
## 100 100 7.479255 0.3010390 6.021474 0.1714964 0.02857132 0.1586523
## 101 101 7.482112 0.3005906 6.022739 0.1747651 0.02903290 0.1611855
## 102 102 7.483790 0.3003332 6.024148 0.1740723 0.02916167 0.1610266
## 103 103 7.482890 0.3005193 6.024295 0.1736236 0.02859756 0.1605440
## 104 104 7.482110 0.3006672 6.023661 0.1740576 0.02861456 0.1591839
## 105 105 7.481867 0.3007146 6.024307 0.1750201 0.02887227 0.1608214
## 106 106 7.481346 0.3008175 6.022547 0.1759497 0.02863438 0.1598421
## 107 107 7.483196 0.3004574 6.024145 0.1776078 0.02870464 0.1620256
## 108 108 7.482300 0.3006477 6.022300 0.1784129 0.02882034 0.1626385
## 109 109 7.485256 0.3001719 6.024954 0.1796772 0.02914643 0.1628090
## 110 110 7.483674 0.3004171 6.025128 0.1799563 0.02877608 0.1627745
## 111 111 7.484664 0.3002268 6.027047 0.1787327 0.02869946 0.1614784
## 112 112 7.483446 0.3004325 6.026097 0.1772230 0.02871677 0.1590364
## 113 113 7.484188 0.3003349 6.026473 0.1758197 0.02847121 0.1564457
## 114 114 7.484664 0.3002658 6.026437 0.1739866 0.02806601 0.1535782
## 115 115 7.485659 0.3001148 6.027212 0.1765701 0.02803581 0.1574654
## 116 116 7.484199 0.3003784 6.026175 0.1801506 0.02858137 0.1600078
## 117 117 7.484125 0.3004072 6.025927 0.1807602 0.02894758 0.1609988
## 118 118 7.484993 0.3002656 6.027299 0.1817604 0.02890271 0.1622072
## 119 119 7.483047 0.3006050 6.026290 0.1809712 0.02872918 0.1612230
## 120 120 7.483482 0.3005433 6.026403 0.1799095 0.02876003 0.1606892
## 121 121 7.482671 0.3006940 6.025047 0.1805174 0.02891212 0.1606966
## 122 122 7.483010 0.3006481 6.024836 0.1797293 0.02885267 0.1611424
## 123 123 7.482224 0.3007689 6.023967 0.1794378 0.02893466 0.1608920
## 124 124 7.481836 0.3008573 6.022704 0.1802692 0.02857173 0.1619554
## 125 125 7.483816 0.3005040 6.023753 0.1801832 0.02847614 0.1616379
## 126 126 7.482962 0.3006794 6.022703 0.1814617 0.02866341 0.1633429
## 127 127 7.481630 0.3009145 6.020357 0.1818332 0.02883275 0.1638307
## 128 128 7.482389 0.3008259 6.021013 0.1801535 0.02895334 0.1617400
## 129 129 7.480541 0.3011358 6.019374 0.1784274 0.02897371 0.1600232
## 130 130 7.483362 0.3006449 6.021410 0.1791271 0.02913294 0.1608850
## 131 131 7.482803 0.3007455 6.021288 0.1785396 0.02893092 0.1610388
## 132 132 7.481532 0.3009688 6.019990 0.1776212 0.02894963 0.1613180
## 133 133 7.481698 0.3009509 6.020429 0.1792799 0.02884122 0.1627747
## 134 134 7.480017 0.3012253 6.019205 0.1787937 0.02898496 0.1622940
## 135 135 7.481661 0.3009406 6.019760 0.1787311 0.02882508 0.1624143
## 136 136 7.482611 0.3007951 6.020850 0.1786075 0.02881681 0.1618344
## 137 137 7.483468 0.3006494 6.021042 0.1782447 0.02877637 0.1613787
## 138 138 7.482659 0.3008218 6.020555 0.1787450 0.02884274 0.1619799
## 139 139 7.482215 0.3008986 6.019897 0.1783934 0.02896343 0.1613003
## 140 140 7.483830 0.3006117 6.020355 0.1805303 0.02910175 0.1628473
## 141 141 7.482191 0.3008989 6.020230 0.1801323 0.02910212 0.1622470
## 142 142 7.480697 0.3011547 6.019394 0.1795645 0.02902624 0.1621871
## 143 143 7.480458 0.3012480 6.018294 0.1797042 0.02937277 0.1614369
## 144 144 7.479869 0.3013593 6.017455 0.1787067 0.02939728 0.1603836
## 145 145 7.479976 0.3013604 6.018339 0.1805318 0.02950715 0.1618075
## 146 146 7.480024 0.3013568 6.017801 0.1803560 0.02928101 0.1611418
## 147 147 7.480107 0.3013416 6.017781 0.1783346 0.02910309 0.1599139
## 148 148 7.479734 0.3014082 6.017579 0.1785546 0.02928448 0.1592946
## 149 149 7.479791 0.3014166 6.016904 0.1797133 0.02926474 0.1606923
## 150 150 7.479542 0.3014304 6.016467 0.1803148 0.02915735 0.1616442
## 151 151 7.479575 0.3014218 6.016125 0.1792466 0.02923373 0.1606687
## 152 152 7.478769 0.3015707 6.015533 0.1802560 0.02947013 0.1621583
## 153 153 7.477732 0.3017484 6.014539 0.1810255 0.02942017 0.1622899
## 154 154 7.478595 0.3016044 6.015158 0.1825205 0.02956332 0.1627891
## 155 155 7.479020 0.3015506 6.015378 0.1822478 0.02966981 0.1622843
## 156 156 7.479629 0.3014638 6.016775 0.1828076 0.02957750 0.1633460
## 157 157 7.480843 0.3012895 6.017028 0.1827252 0.02979611 0.1632318
## 158 158 7.481197 0.3012458 6.017609 0.1833259 0.02995381 0.1629606
## 159 159 7.480643 0.3013310 6.017447 0.1843135 0.02990201 0.1647745
## 160 160 7.480214 0.3014178 6.016711 0.1843823 0.02996034 0.1637489
## 161 161 7.481125 0.3012677 6.017056 0.1843600 0.03000321 0.1638921
## 162 162 7.480700 0.3013204 6.016865 0.1846245 0.02977113 0.1637432
## 163 163 7.481151 0.3012515 6.017233 0.1841268 0.02970776 0.1638990
## 164 164 7.481795 0.3011460 6.017561 0.1836578 0.02963372 0.1640277
## 165 165 7.480725 0.3013193 6.016279 0.1845021 0.02970229 0.1634430
## 166 166 7.482433 0.3010411 6.017500 0.1854936 0.02993450 0.1636793
## 167 167 7.481885 0.3011517 6.016891 0.1844574 0.02998097 0.1622699
## 168 168 7.480971 0.3013090 6.016889 0.1848040 0.03012414 0.1626239
## 169 169 7.481340 0.3012496 6.017076 0.1838363 0.03000969 0.1613722
## 170 170 7.480510 0.3013769 6.016831 0.1831698 0.02994864 0.1601393
## 171 171 7.480057 0.3014375 6.015610 0.1834646 0.02991675 0.1601815
## 172 172 7.480177 0.3014369 6.015224 0.1833118 0.02989406 0.1599121
## 173 173 7.480622 0.3013630 6.015025 0.1824693 0.02988696 0.1593063
## 174 174 7.480554 0.3013910 6.015201 0.1823655 0.02988189 0.1598790
## 175 175 7.480668 0.3013549 6.014823 0.1824456 0.02981618 0.1605595
## 176 176 7.481786 0.3011552 6.015112 0.1818679 0.02969456 0.1603792
## 177 177 7.482593 0.3010105 6.015362 0.1812401 0.02959799 0.1601268
## 178 178 7.483822 0.3007954 6.016100 0.1817021 0.02951033 0.1600102
## 179 179 7.484877 0.3006134 6.017150 0.1808875 0.02932040 0.1602670
## 180 180 7.484898 0.3006199 6.017168 0.1816937 0.02941835 0.1609758
## 181 181 7.485763 0.3004812 6.017115 0.1823299 0.02956303 0.1615338
## 182 182 7.485404 0.3005351 6.017368 0.1832483 0.02975653 0.1620426
## 183 183 7.484354 0.3007119 6.016975 0.1837193 0.02970635 0.1626210
## 184 184 7.485183 0.3005817 6.018041 0.1824799 0.02973094 0.1612029
## 185 185 7.485048 0.3005934 6.017920 0.1830404 0.02973858 0.1619881
## 186 186 7.485073 0.3005814 6.018437 0.1829973 0.02980301 0.1617574
## 187 187 7.484021 0.3007685 6.017982 0.1825476 0.02971558 0.1614150
## 188 188 7.483921 0.3007873 6.018009 0.1822752 0.02968734 0.1614929
## 189 189 7.484521 0.3006749 6.017884 0.1829456 0.02962028 0.1617071
## 190 190 7.483636 0.3008212 6.017520 0.1831152 0.02968078 0.1614157
## 191 191 7.483409 0.3008699 6.017511 0.1832174 0.02965141 0.1617752
## 192 192 7.483465 0.3008526 6.017611 0.1819918 0.02947164 0.1606960
## 193 193 7.483885 0.3007819 6.018391 0.1815606 0.02929518 0.1605203
## 194 194 7.483362 0.3008833 6.018452 0.1814657 0.02928589 0.1606683
## 195 195 7.483432 0.3008634 6.018303 0.1811837 0.02913594 0.1604291
## 196 196 7.483396 0.3008739 6.018367 0.1816608 0.02915778 0.1613072
## 197 197 7.483461 0.3008642 6.018659 0.1815574 0.02911527 0.1613200
## 198 198 7.483013 0.3009407 6.018075 0.1815177 0.02914239 0.1612210
## 199 199 7.483845 0.3007964 6.018840 0.1808738 0.02907523 0.1608688
## 200 200 7.483382 0.3008748 6.018817 0.1809624 0.02901449 0.1606217
## 201 201 7.483404 0.3008824 6.018834 0.1811594 0.02910871 0.1607222
## 202 202 7.483452 0.3008719 6.018984 0.1815288 0.02902675 0.1608282
## 203 203 7.483470 0.3008647 6.019293 0.1814845 0.02895399 0.1610469
## 204 204 7.483601 0.3008427 6.019563 0.1815816 0.02890602 0.1615091
## 205 205 7.483740 0.3008149 6.019724 0.1818313 0.02899339 0.1616071
## 206 206 7.483537 0.3008553 6.019279 0.1821229 0.02892115 0.1615915
## 207 207 7.483901 0.3007908 6.019490 0.1822011 0.02901238 0.1614157
## 208 208 7.484019 0.3007693 6.019467 0.1823397 0.02909964 0.1615723
## 209 209 7.483540 0.3008500 6.018840 0.1826541 0.02908732 0.1618458
## 210 210 7.483971 0.3007809 6.019545 0.1827883 0.02914808 0.1619694
## 211 211 7.484121 0.3007603 6.019852 0.1823399 0.02909640 0.1616234
## 212 212 7.484250 0.3007415 6.020120 0.1825087 0.02913662 0.1617496
## 213 213 7.484379 0.3007170 6.020145 0.1822955 0.02907863 0.1615959
## 214 214 7.484604 0.3006784 6.020415 0.1825382 0.02909632 0.1615204
## 215 215 7.485048 0.3006065 6.020643 0.1829020 0.02904780 0.1617774
## 216 216 7.485361 0.3005550 6.021120 0.1822924 0.02901831 0.1615135
## 217 217 7.485561 0.3005281 6.021225 0.1819005 0.02904631 0.1612285
## 218 218 7.485676 0.3005104 6.021167 0.1817411 0.02897691 0.1611274
## 219 219 7.485472 0.3005418 6.020945 0.1820197 0.02898308 0.1614550
## 220 220 7.485475 0.3005451 6.021077 0.1817088 0.02894589 0.1611838
## 221 221 7.485310 0.3005733 6.021241 0.1816350 0.02895481 0.1611551
## 222 222 7.485330 0.3005696 6.021271 0.1815238 0.02892491 0.1613296
## 223 223 7.485533 0.3005342 6.021232 0.1815954 0.02897600 0.1614133
## 224 224 7.485393 0.3005600 6.021279 0.1816679 0.02897485 0.1615359
## 225 225 7.485507 0.3005400 6.021474 0.1814428 0.02895719 0.1614197
## 226 226 7.485774 0.3004966 6.021803 0.1813580 0.02901401 0.1612259
## 227 227 7.485958 0.3004613 6.022085 0.1812933 0.02899000 0.1609602
## 228 228 7.485903 0.3004686 6.022126 0.1813543 0.02896481 0.1611853
## 229 229 7.485865 0.3004746 6.022094 0.1813131 0.02895796 0.1611545
## 230 230 7.485790 0.3004865 6.022059 0.1810982 0.02895731 0.1609416
## 231 231 7.485821 0.3004832 6.021956 0.1811233 0.02894285 0.1608900
## 232 232 7.485821 0.3004816 6.021906 0.1812322 0.02891255 0.1609931
## 233 233 7.485961 0.3004561 6.022020 0.1813644 0.02891960 0.1609937
## 234 234 7.486026 0.3004439 6.022105 0.1813039 0.02891242 0.1609771
## 235 235 7.486028 0.3004447 6.022125 0.1813252 0.02891942 0.1610493
## 236 236 7.486036 0.3004439 6.022118 0.1812701 0.02891840 0.1610519
## 237 237 7.486065 0.3004386 6.022129 0.1812642 0.02891534 0.1610839
## 238 238 7.486033 0.3004442 6.022107 0.1812514 0.02891806 0.1610742
## 239 239 7.486022 0.3004460 6.022095 0.1812484 0.02891771 0.1610637
## 240 240 7.486026 0.3004453 6.022099 0.1812374 0.02891745 0.1610546
## nvmax
## 26 26
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x8 x9
## 8.775478e+01 -1.571182e-02 3.407441e+00 1.528822e-01 9.308719e-01
## x10 x11 x16 x17 x21
## 4.672910e-01 5.305842e+07 2.971064e-01 4.228913e-01 2.603049e-02
## stat4 stat6 stat14 stat23 stat33
## -1.599196e-01 -1.414834e-01 -2.568627e-01 1.404007e-01 -1.696432e-01
## stat35 stat38 stat41 stat50 stat98
## -1.425793e-01 1.920227e-01 -1.584774e-01 1.373511e-01 9.462024e-01
## stat100 stat110 stat144 stat149 stat156
## 1.862947e-01 -9.004465e-01 1.464019e-01 -1.907112e-01 2.131350e-01
## stat172 sqrt.x18
## 1.709260e-01 7.329063e+00Test
if (algo.backward.caret == TRUE){
test.model(model.backward, data.test
,method = 'leapBackward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 106.0 120.8 124.2 124.2 128.0 139.0
## [1] "leapBackward Test MSE: 94.0858683870622"
Stepwise Selection (w/ full train)
Train
if (algo.stepwise == TRUE){
t1 = Sys.time()
model.stepwise = step(model.null, scope=list(upper=model.full), data = data.train, direction="both", trace = 0)
print(summary(model.stepwise))
t2 = Sys.time()
print (paste("Time taken for Stepwise Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.stepwise, data.train)
}Test
if (algo.stepwise == TRUE){
test.model(model.stepwise, data.test, "Stepwise Selection")
}Stepwise Selection (w/ filtered train)
Train
if (algo.stepwise == TRUE){
t1 = Sys.time()
model.stepwise2 = step(model.null2, scope=list(upper=model.full2), data = data.train2, direction="both", trace = 0)
print(summary(model.stepwise2))
t2 = Sys.time()
print (paste("Time taken for Stepwise Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.stepwise2, data.train2)
}Test
if (algo.stepwise == TRUE){
test.model(model.stepwise2, data.test, "Stepwise Selection (2)")
}Stepwise Selection with CV (w/ full train)
Train
if (algo.stepwise.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "leapSeq"
,feature.names = feature.names)
model.stepwise = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 9 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 10.210693 0.1018765 7.806589 0.3486064 0.02595312 0.1962709
## 2 2 9.979515 0.1418363 7.597398 0.3207096 0.02875352 0.1777050
## 3 3 9.814897 0.1693658 7.438338 0.3290643 0.02785753 0.1807549
## 4 4 9.667493 0.1938902 7.231990 0.3201540 0.02682717 0.1803033
## 5 5 9.588875 0.2068466 7.172167 0.3419604 0.02484619 0.1975346
## 6 6 9.591660 0.2066208 7.174795 0.3521685 0.02739708 0.2020679
## 7 7 9.571737 0.2099116 7.168707 0.3525200 0.02833739 0.2076171
## 8 8 9.546348 0.2140867 7.150908 0.3571826 0.02810121 0.2058362
## 9 9 9.521497 0.2180754 7.135342 0.3515932 0.02753943 0.1919503
## 10 10 9.531634 0.2164981 7.142145 0.3502059 0.02798899 0.1947027
## 11 11 9.531499 0.2165176 7.142129 0.3493156 0.02735269 0.1916070
## 12 12 9.541275 0.2149978 7.148145 0.3511383 0.02743081 0.1879036
## 13 13 9.539867 0.2151993 7.145155 0.3456114 0.02644858 0.1873780
## 14 14 9.532611 0.2163848 7.137358 0.3470188 0.02761253 0.1915646
## 15 15 9.525866 0.2174740 7.126506 0.3478493 0.02822187 0.1955673
## 16 16 9.532841 0.2163902 7.129214 0.3525212 0.02855943 0.1972010
## 17 17 9.534851 0.2160552 7.129357 0.3505236 0.02748267 0.1945657
## 18 18 9.647482 0.1976113 7.222395 0.5634864 0.05809294 0.3731880
## 19 19 9.535883 0.2159502 7.135435 0.3475181 0.02829213 0.1958929
## 20 20 9.537824 0.2156687 7.135493 0.3478763 0.02871909 0.1898675
## 21 21 9.536909 0.2159019 7.132231 0.3588146 0.02965921 0.1943078
## 22 22 9.545095 0.2146063 7.140942 0.3574051 0.02982239 0.1961330
## 23 23 9.545275 0.2145559 7.138831 0.3525479 0.02889786 0.1906030
## 24 24 9.717224 0.1836663 7.281704 0.3765159 0.07388510 0.2996599
## 25 25 9.557141 0.2127138 7.149969 0.3600224 0.02911233 0.1922357
## 26 26 9.657701 0.1951223 7.242387 0.4177494 0.05878111 0.2501277
## 27 27 9.644232 0.1981933 7.248369 0.5398596 0.05884316 0.3825956
## 28 28 9.671118 0.1930862 7.249987 0.4163837 0.05811065 0.2496969
## 29 29 9.660115 0.1957328 7.256034 0.5328809 0.05786791 0.3778188
## 30 30 9.794855 0.1715607 7.341636 0.5014443 0.07653227 0.3526236
## 31 31 9.687563 0.1904735 7.265854 0.3524549 0.05356438 0.2561924
## 32 32 9.600078 0.2063063 7.183936 0.3681083 0.02950020 0.2058563
## 33 33 9.832761 0.1669749 7.402819 0.5658021 0.06568036 0.4382143
## 34 34 9.696713 0.1899830 7.259124 0.4381303 0.04971971 0.3112667
## 35 35 9.676930 0.1924304 7.234979 0.4005574 0.05922545 0.2657224
## 36 36 9.774703 0.1755089 7.343104 0.4179294 0.06558233 0.2740389
## 37 37 9.857103 0.1625069 7.399391 0.5171231 0.06946431 0.3631828
## 38 38 9.716166 0.1869891 7.283507 0.3289277 0.03682744 0.2674317
## 39 39 9.625348 0.2025005 7.201939 0.3564929 0.02785892 0.2126164
## 40 40 9.721902 0.1861499 7.290145 0.3277698 0.03701839 0.2699830
## 41 41 9.723392 0.1859487 7.292611 0.3247149 0.03584246 0.2679377
## 42 42 9.717711 0.1868936 7.278478 0.4324117 0.04838245 0.3080909
## 43 43 9.710256 0.1875095 7.283628 0.3831264 0.04760399 0.2559135
## 44 44 9.645711 0.1994672 7.218879 0.3610519 0.02744794 0.2109379
## 45 45 9.647598 0.1992497 7.225484 0.3565794 0.02726203 0.2118465
## 46 46 9.734595 0.1844265 7.294750 0.4279132 0.04825293 0.3066137
## 47 47 9.723054 0.1865137 7.299665 0.5154828 0.05425497 0.3675418
## 48 48 9.655069 0.1981808 7.227297 0.3558743 0.02826220 0.2144426
## 49 49 9.835732 0.1662450 7.398791 0.3731696 0.06691250 0.2673416
## 50 50 9.787152 0.1750603 7.349467 0.4611299 0.05724995 0.3195169
## 51 51 9.849494 0.1658094 7.389141 0.4960428 0.05263783 0.3739671
## 52 52 9.756176 0.1808342 7.314441 0.4061820 0.05499754 0.2496784
## 53 53 9.831005 0.1679537 7.375453 0.5393295 0.06236499 0.3689008
## 54 54 9.755043 0.1814820 7.311708 0.4294747 0.04861528 0.3078929
## 55 55 9.676824 0.1950033 7.251022 0.3571696 0.02741171 0.2117132
## 56 56 9.829988 0.1687042 7.404419 0.4760534 0.05426128 0.3804220
## 57 57 9.750295 0.1824792 7.299991 0.4607139 0.05059320 0.3137337
## 58 58 9.891144 0.1583930 7.431578 0.4738673 0.05649229 0.3700848
## 59 59 9.828104 0.1697498 7.365790 0.5919676 0.06038724 0.3976078
## 60 60 9.764688 0.1801452 7.329783 0.3207330 0.03417814 0.2563213
## 61 61 9.897464 0.1563062 7.467153 0.4580327 0.05911674 0.3579670
## 62 62 9.767811 0.1790226 7.344051 0.3487030 0.05202713 0.2608407
## 63 63 9.749721 0.1826901 7.328246 0.4998323 0.05157607 0.3503903
## 64 64 9.748426 0.1823902 7.300094 0.3970540 0.05660611 0.2579311
## 65 65 9.780192 0.1782103 7.325805 0.5339025 0.05227522 0.3560707
## 66 66 9.840612 0.1658720 7.412150 0.3542917 0.06150612 0.2654338
## 67 67 9.831618 0.1682829 7.359183 0.4450118 0.06537331 0.3230012
## 68 68 9.689276 0.1934971 7.258654 0.3575813 0.02740414 0.2105906
## 69 69 9.890378 0.1582400 7.409822 0.5027955 0.07046284 0.3646972
## 70 70 9.686359 0.1939752 7.256419 0.3586914 0.02777221 0.2084844
## 71 71 9.840229 0.1665679 7.401339 0.3250981 0.04656283 0.2625258
## 72 72 9.778365 0.1782843 7.340713 0.3230135 0.03445577 0.2583162
## 73 73 9.930812 0.1523014 7.433985 0.5793714 0.07510932 0.4180765
## 74 74 9.865865 0.1626748 7.433214 0.2858501 0.05283743 0.2735927
## 75 75 9.886066 0.1596422 7.437647 0.6211988 0.07038555 0.4524209
## 76 76 9.692816 0.1931202 7.260681 0.3576133 0.02727025 0.2061332
## 77 77 10.000591 0.1391080 7.546188 0.4304241 0.07455112 0.3710366
## 78 78 9.835177 0.1682056 7.362040 0.4517959 0.06718730 0.3265192
## 79 79 9.844877 0.1663995 7.386900 0.3476859 0.05835906 0.2890495
## 80 80 9.844261 0.1676607 7.383627 0.5948055 0.06148588 0.4065115
## 81 81 9.774349 0.1792229 7.319887 0.4276916 0.04905973 0.2916783
## 82 82 9.693152 0.1931613 7.262203 0.3492307 0.02645065 0.1994268
## 83 83 10.071883 0.1277877 7.598961 0.6379805 0.08436497 0.4653148
## 84 84 9.855391 0.1655772 7.384057 0.5015271 0.06118019 0.3611131
## 85 85 9.795960 0.1763722 7.338596 0.5404296 0.05292698 0.3572979
## 86 86 9.862577 0.1632011 7.421981 0.3739732 0.06608772 0.2576548
## 87 87 9.761136 0.1817250 7.334105 0.5151351 0.05386527 0.3701072
## 88 88 9.757186 0.1813037 7.324867 0.3788432 0.04662194 0.2410340
## 89 89 9.772962 0.1797818 7.328013 0.4557305 0.05029645 0.3133496
## 90 90 9.939313 0.1508687 7.454678 0.5054536 0.07020713 0.3614883
## 91 91 9.761461 0.1811726 7.312079 0.3982146 0.05761853 0.2621458
## 92 92 10.030463 0.1360867 7.540274 0.5564309 0.06130058 0.4292424
## 93 93 9.787943 0.1768506 7.362820 0.3516935 0.05426362 0.2597708
## 94 94 9.871177 0.1623229 7.421083 0.3416119 0.05319531 0.2629935
## 95 95 9.913995 0.1551337 7.450939 0.5361369 0.07026933 0.3732416
## 96 96 9.695032 0.1930889 7.266328 0.3525717 0.02756996 0.2030230
## 97 97 9.774232 0.1797251 7.329896 0.4626252 0.05225970 0.3219442
## 98 98 9.893412 0.1576035 7.448936 0.5423576 0.07814594 0.4007313
## 99 99 9.849349 0.1658752 7.398554 0.3451775 0.05897299 0.2934096
## 100 100 9.773929 0.1797501 7.330873 0.4592287 0.05185824 0.3212672
## 101 101 9.773781 0.1790262 7.332116 0.3893570 0.05233276 0.2315125
## 102 102 9.900030 0.1572896 7.430891 0.5065422 0.07254343 0.3745622
## 103 103 9.759151 0.1809886 7.329962 0.3720960 0.04717366 0.2405385
## 104 104 9.822466 0.1713740 7.392299 0.5827497 0.06377489 0.4243202
## 105 105 9.692485 0.1935218 7.263099 0.3479311 0.02714981 0.2008952
## 106 106 9.751788 0.1832182 7.315561 0.4463723 0.04519888 0.2951149
## 107 107 9.697418 0.1928149 7.266630 0.3515465 0.02691013 0.2054843
## 108 108 9.777965 0.1790130 7.323488 0.4268141 0.04912295 0.2979911
## 109 109 9.935831 0.1527820 7.454163 0.6466476 0.07025572 0.4644751
## 110 110 9.780302 0.1783284 7.330279 0.3932594 0.05278178 0.2401388
## 111 111 9.893726 0.1588365 7.441268 0.5939926 0.07713887 0.4413163
## 112 112 9.783504 0.1783068 7.329216 0.4227898 0.04892593 0.2959643
## 113 113 9.954299 0.1486873 7.494710 0.4461486 0.07443988 0.3364205
## 114 114 9.701158 0.1924194 7.268522 0.3417138 0.02602544 0.2016328
## 115 115 9.850239 0.1658566 7.390447 0.4231732 0.05885308 0.3064241
## 116 116 9.701022 0.1924449 7.265780 0.3392646 0.02614051 0.1988495
## 117 117 10.052876 0.1330077 7.540090 0.6098548 0.07497737 0.4464442
## 118 118 9.865622 0.1645435 7.439502 0.5138075 0.07024533 0.4060229
## 119 119 9.701963 0.1923261 7.267226 0.3378624 0.02598477 0.1966654
## 120 120 9.779066 0.1789664 7.348713 0.2997716 0.03224976 0.2558880
## 121 121 9.781603 0.1786583 7.349546 0.3007157 0.03227666 0.2534222
## 122 122 9.873110 0.1610296 7.442391 0.3335004 0.06450190 0.2502558
## 123 123 9.849421 0.1667454 7.404453 0.3109422 0.04565980 0.2519778
## 124 124 9.703076 0.1922369 7.272107 0.3360867 0.02644129 0.1941471
## 125 125 9.703337 0.1922342 7.273153 0.3377555 0.02660750 0.1954605
## 126 126 9.749222 0.1836123 7.315994 0.3649733 0.04946376 0.2537729
## 127 127 9.741367 0.1854247 7.305778 0.4020450 0.03761414 0.2533949
## 128 128 9.850252 0.1672402 7.407681 0.4666898 0.05528211 0.3284501
## 129 129 9.863339 0.1632330 7.420674 0.4200658 0.06304436 0.3080222
## 130 130 10.013132 0.1375602 7.562646 0.2938597 0.06181808 0.2514869
## 131 131 9.741807 0.1853440 7.308655 0.4061928 0.03855058 0.2545514
## 132 132 9.811918 0.1732964 7.349678 0.4084036 0.04848722 0.2557072
## 133 133 9.809779 0.1733993 7.379254 0.3028879 0.04332713 0.2509722
## 134 134 9.808822 0.1735905 7.385087 0.2797429 0.03270282 0.2138170
## 135 135 9.700595 0.1927172 7.272141 0.3392941 0.02626019 0.1960024
## 136 136 9.847892 0.1661019 7.403167 0.3184326 0.05670888 0.2104035
## 137 137 9.776589 0.1789378 7.348318 0.3842219 0.04041236 0.2373584
## 138 138 9.701426 0.1926678 7.272380 0.3400900 0.02662710 0.1966177
## 139 139 9.832061 0.1711776 7.366421 0.4961808 0.04741722 0.3239161
## 140 140 9.740957 0.1852461 7.312324 0.3555060 0.04482339 0.2409866
## 141 141 9.735062 0.1866462 7.299774 0.3860929 0.03519673 0.2326746
## 142 142 9.703176 0.1924256 7.273295 0.3356725 0.02643224 0.1913767
## 143 143 9.742979 0.1849784 7.314233 0.3525888 0.04445209 0.2375254
## 144 144 9.765043 0.1817362 7.334227 0.3168716 0.04169471 0.1961039
## 145 145 9.705307 0.1921773 7.276121 0.3356178 0.02671873 0.1895523
## 146 146 9.755925 0.1840724 7.338725 0.4520656 0.04304802 0.3328733
## 147 147 9.775013 0.1802629 7.345276 0.2929510 0.02889300 0.2211458
## 148 148 9.753636 0.1839108 7.316781 0.3603562 0.03445509 0.2383081
## 149 149 9.800031 0.1760471 7.391642 0.4449877 0.04737184 0.3293616
## 150 150 9.748628 0.1842598 7.327611 0.3326971 0.03513098 0.1983941
## 151 151 9.706870 0.1919910 7.277071 0.3382122 0.02706506 0.1892865
## 152 152 9.757459 0.1828033 7.311885 0.3458595 0.03930539 0.1871026
## 153 153 9.704477 0.1923266 7.275587 0.3382009 0.02706117 0.1907077
## 154 154 9.772198 0.1811246 7.328147 0.4228137 0.04461907 0.2795167
## 155 155 9.768658 0.1818517 7.320530 0.4414057 0.03645477 0.2676989
## 156 156 9.767250 0.1814688 7.335802 0.3189711 0.04230599 0.1987931
## 157 157 9.822920 0.1720471 7.384154 0.3127435 0.03408248 0.2603302
## 158 158 9.851346 0.1674205 7.385357 0.4889294 0.04452955 0.3208031
## 159 159 9.770662 0.1815350 7.320686 0.4451912 0.03677697 0.2712851
## 160 160 9.703530 0.1924203 7.272036 0.3406528 0.02676740 0.1911692
## 161 161 9.768353 0.1812838 7.334101 0.3203554 0.04239639 0.1985483
## 162 162 9.768602 0.1818484 7.317724 0.4457636 0.03691740 0.2711759
## 163 163 9.737757 0.1863253 7.297423 0.3923978 0.03603293 0.2275220
## 164 164 9.770729 0.1813514 7.324596 0.4250056 0.04470633 0.2811342
## 165 165 9.769669 0.1817289 7.318516 0.4474700 0.03694440 0.2713981
## 166 166 9.703860 0.1924260 7.274302 0.3417772 0.02670273 0.1913416
## 167 167 9.832479 0.1701161 7.390410 0.2991708 0.03956703 0.2203344
## 168 168 9.703266 0.1925020 7.274114 0.3418229 0.02707552 0.1902636
## 169 169 9.769317 0.1817805 7.320543 0.4473617 0.03693371 0.2703031
## 170 170 9.767026 0.1816034 7.334321 0.3234128 0.04257129 0.1986012
## 171 171 9.751993 0.1847402 7.336796 0.4566331 0.04338052 0.3360787
## 172 172 9.839283 0.1703245 7.374352 0.5103196 0.04955599 0.3403145
## 173 173 9.817456 0.1729419 7.379632 0.4234971 0.04966061 0.2879792
## 174 174 9.700346 0.1929592 7.271243 0.3434638 0.02733004 0.1933120
## 175 175 9.840646 0.1691407 7.405489 0.2584515 0.04142277 0.2137386
## 176 176 9.815724 0.1734090 7.377172 0.4450340 0.04282132 0.2829553
## 177 177 9.883694 0.1622727 7.422592 0.4318052 0.04843592 0.2946384
## 178 178 9.703302 0.1925764 7.273377 0.3455844 0.02739900 0.1954381
## 179 179 9.767190 0.1817120 7.332937 0.3255151 0.04217534 0.1976953
## 180 180 9.749030 0.1843008 7.326404 0.3439113 0.03603598 0.2065651
## 181 181 9.862745 0.1649615 7.416957 0.3734161 0.05038234 0.2851458
## 182 182 9.704905 0.1923527 7.274961 0.3465582 0.02750230 0.1962965
## 183 183 9.748813 0.1843504 7.325827 0.3453018 0.03626261 0.2069183
## 184 184 9.704279 0.1924540 7.274862 0.3467134 0.02768583 0.1961520
## 185 185 9.815369 0.1740385 7.357137 0.5014352 0.04518725 0.3140779
## 186 186 9.705617 0.1922660 7.275475 0.3456350 0.02736520 0.1959619
## 187 187 9.894543 0.1601273 7.452993 0.3607078 0.04533346 0.2823943
## 188 188 9.781137 0.1795707 7.349705 0.3062776 0.03133338 0.2361290
## 189 189 9.749546 0.1847112 7.306970 0.4149401 0.03930423 0.2443050
## 190 190 9.705897 0.1921961 7.275586 0.3440982 0.02718558 0.1946897
## 191 191 9.754991 0.1843633 7.338171 0.4554987 0.04301294 0.3365069
## 192 192 9.808474 0.1743505 7.352347 0.4173092 0.04812185 0.2385322
## 193 193 9.708116 0.1918683 7.276533 0.3432657 0.02698029 0.1949423
## 194 194 9.821911 0.1726158 7.380334 0.4439018 0.04260161 0.2839781
## 195 195 9.806780 0.1743419 7.360756 0.3709727 0.05415419 0.2407021
## 196 196 9.866339 0.1656805 7.430827 0.5412073 0.05892743 0.4031245
## 197 197 9.773751 0.1812483 7.326601 0.4515810 0.03763095 0.2845253
## 198 198 9.706930 0.1920668 7.275930 0.3439761 0.02702817 0.1951985
## 199 199 9.852996 0.1678184 7.403670 0.3853046 0.04433907 0.3049050
## 200 200 9.773950 0.1812211 7.326940 0.4517410 0.03760941 0.2836997
## 201 201 9.762098 0.1827311 7.320500 0.3759972 0.03710584 0.2534567
## 202 202 9.833842 0.1703039 7.408169 0.2914936 0.03739392 0.2348183
## 203 203 9.706883 0.1920770 7.275996 0.3427294 0.02698645 0.1947452
## 204 204 9.706918 0.1920745 7.276343 0.3418284 0.02690730 0.1944711
## 205 205 9.766026 0.1817595 7.324016 0.3577664 0.04173812 0.2032299
## 206 206 9.707184 0.1920361 7.276479 0.3417104 0.02686366 0.1949266
## 207 207 9.840655 0.1696221 7.396774 0.3253917 0.03718039 0.2750289
## 208 208 9.786022 0.1788652 7.354301 0.3056369 0.03211498 0.2431751
## 209 209 9.765339 0.1818839 7.322790 0.3574270 0.04151411 0.2018226
## 210 210 9.774758 0.1807605 7.340982 0.3250467 0.04321890 0.2046218
## 211 211 9.707230 0.1920369 7.276482 0.3420154 0.02689284 0.1944229
## 212 212 9.759323 0.1839069 7.341299 0.4631202 0.04377468 0.3410788
## 213 213 9.707529 0.1919811 7.276811 0.3422276 0.02685810 0.1946583
## 214 214 9.706872 0.1920779 7.276312 0.3421563 0.02687518 0.1946258
## 215 215 9.707125 0.1920436 7.276537 0.3423709 0.02688269 0.1948166
## 216 216 9.707523 0.1919918 7.276899 0.3421291 0.02683683 0.1943649
## 217 217 9.777332 0.1807731 7.331015 0.4568853 0.03850126 0.2918640
## 218 218 9.775042 0.1807149 7.342036 0.3250848 0.04327753 0.2061948
## 219 219 9.707181 0.1920418 7.276845 0.3416553 0.02685859 0.1940363
## 220 220 9.707422 0.1920032 7.277065 0.3416047 0.02686831 0.1939818
## 221 221 9.707501 0.1919944 7.277178 0.3418774 0.02689742 0.1940936
## 222 222 9.707310 0.1920203 7.276914 0.3417660 0.02687880 0.1941040
## 223 223 9.755694 0.1833930 7.331231 0.3413574 0.03667873 0.2076602
## 224 224 9.774799 0.1807750 7.342142 0.3245774 0.04307101 0.2058221
## 225 225 9.820513 0.1728980 7.384374 0.3422645 0.05609527 0.2480655
## 226 226 9.707045 0.1920539 7.276607 0.3416431 0.02685931 0.1943162
## 227 227 9.769509 0.1814068 7.327492 0.3616857 0.04273247 0.2079439
## 228 228 9.706890 0.1920750 7.276668 0.3413004 0.02681223 0.1939648
## 229 229 9.706862 0.1920806 7.276604 0.3413612 0.02683954 0.1941413
## 230 230 9.706910 0.1920736 7.276613 0.3413077 0.02683709 0.1940985
## 231 231 9.780208 0.1804681 7.332348 0.4646151 0.03940813 0.2962378
## 232 232 9.844655 0.1688144 7.403243 0.3083319 0.04186990 0.2325107
## 233 233 9.706781 0.1920922 7.276498 0.3415837 0.02685977 0.1943116
## 234 234 9.813055 0.1746719 7.400711 0.4659393 0.05004793 0.3469729
## 235 235 9.706860 0.1920807 7.276577 0.3414986 0.02684323 0.1942913
## 236 236 9.899372 0.1611273 7.456086 0.4997876 0.06083318 0.3755565
## 237 237 9.957488 0.1501580 7.508795 0.2790800 0.05400183 0.2731042
## 238 238 9.882259 0.1640424 7.444013 0.5652339 0.06141656 0.4221842
## 239 239 9.888525 0.1620613 7.474164 0.4193487 0.04748758 0.3416164
## 240 240 9.706892 0.1920777 7.276628 0.3415893 0.02684758 0.1943579
## nvmax
## 9 9
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x9 x10 x16
## 96.70112202 -0.01426636 3.24632946 0.95963994 0.38442187 0.28876623
## x17 stat98 stat110 sqrt.x18
## 0.43713626 1.02673501 -0.96934667 7.48687040Test
if (algo.stepwise.caret == TRUE){
test.model(model.stepwise, data.test
,method = 'leapSeq',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 109.7 122.1 125.4 125.4 129.1 142.0
## [1] "leapSeq Test MSE: 93.4589126618511"
Stepwise Selection with CV (w/ filtered train)
Train
Test
LASSO (w/ full train)
Train
if(algo.LASSO == TRUE){
# Formatting data for GLM net
# you can use model.matrix as well -- model.matrix creates a design (or model) matrix,
# e.g., by expanding factors to a set of dummy variables (depending on the contrasts)
# and expanding interactions similarly.
x = as.matrix(data.train[,feature.names])
y = data.train[,label.names]
xtest = as.matrix(data.test[,feature.names])
ytest = data.test[,label.names]
grid=10^seq(10,-2, length =100)
set.seed(1)
model.LASSO=glmnet(x,y,alpha=1, lambda =grid)
cv.out=cv.glmnet(x,y,alpha=1) # alpha=1 performs LASSO
plot(cv.out)
bestlambda<-cv.out$lambda.min # Optimal penalty parameter. You can make this call visually.
print(coef(model.LASSO,s=bestlambda))
}Test
if(algo.LASSO == TRUE){
lasso.pred=predict (model.LASSO ,s=bestlambda ,newx=xtest)
testMSE_LASSO = mean((ytest-lasso.pred)^2)
print (paste("LASSO Test RMSE: ",testMSE_LASSO, sep=""))
plot(ytest,lasso.pred)
}LASSO (w/ filtered train)
Train
if(algo.LASSO == TRUE){
# Formatting data for GLM net
# you can use model.matrix as well -- model.matrix creates a design (or model) matrix,
# e.g., by expanding factors to a set of dummy variables (depending on the contrasts)
# and expanding interactions similarly.
x = as.matrix(data.train2[,feature.names])
y = data.train2[,label.names]
xtest = as.matrix(data.test[,feature.names])
ytest = data.test[,label.names]
grid=10^seq(10,-2, length =100)
set.seed(1)
model.LASSO=glmnet(x,y,alpha=1, lambda =grid)
cv.out=cv.glmnet(x,y,alpha=1) # alpha=1 performs LASSO
plot(cv.out)
bestlambda<-cv.out$lambda.min # Optimal penalty parameter. You can make this call visually.
print(coef(model.LASSO,s=bestlambda))
}Test
if(algo.LASSO == TRUE){
lasso.pred=predict (model.LASSO ,s=bestlambda ,newx=xtest)
testMSE_LASSO = mean((ytest-lasso.pred)^2)
print (paste("LASSO Test RMSE: ",testMSE_LASSO, sep=""))
plot(ytest,lasso.pred)
}LASSO with CV (w/ full train)
Train
if (algo.LASSO.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "glmnet"
,subopt = 'LASSO'
,feature.names = feature.names)
model.LASSO.caret = returned$model
}## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.206 on full training set
## glmnet
##
## 6002 samples
## 240 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5402, 5401, 5402, 5401, 5402, 5402, ...
## Resampling results across tuning parameters:
##
## lambda RMSE Rsquared MAE
## 0.01000000 9.683314 0.1947497 7.258914
## 0.01047616 9.682252 0.1948730 7.258121
## 0.01097499 9.681149 0.1950015 7.257291
## 0.01149757 9.680001 0.1951353 7.256432
## 0.01204504 9.678811 0.1952741 7.255538
## 0.01261857 9.677573 0.1954190 7.254600
## 0.01321941 9.676283 0.1955702 7.253624
## 0.01384886 9.674944 0.1957276 7.252616
## 0.01450829 9.673560 0.1958905 7.251587
## 0.01519911 9.672134 0.1960584 7.250540
## 0.01592283 9.670659 0.1962327 7.249462
## 0.01668101 9.669132 0.1964136 7.248342
## 0.01747528 9.667530 0.1966044 7.247163
## 0.01830738 9.665859 0.1968043 7.245919
## 0.01917910 9.664136 0.1970110 7.244639
## 0.02009233 9.662350 0.1972262 7.243311
## 0.02104904 9.660489 0.1974516 7.241930
## 0.02205131 9.658565 0.1976854 7.240499
## 0.02310130 9.656594 0.1979257 7.239038
## 0.02420128 9.654557 0.1981749 7.237552
## 0.02535364 9.652449 0.1984343 7.236054
## 0.02656088 9.650271 0.1987039 7.234512
## 0.02782559 9.647993 0.1989883 7.232919
## 0.02915053 9.645617 0.1992872 7.231258
## 0.03053856 9.643192 0.1995933 7.229531
## 0.03199267 9.640731 0.1999053 7.227750
## 0.03351603 9.638243 0.2002211 7.225966
## 0.03511192 9.635698 0.2005461 7.224152
## 0.03678380 9.633099 0.2008795 7.222309
## 0.03853529 9.630444 0.2012218 7.220415
## 0.04037017 9.627751 0.2015710 7.218525
## 0.04229243 9.625034 0.2019255 7.216609
## 0.04430621 9.622280 0.2022876 7.214684
## 0.04641589 9.619490 0.2026574 7.212712
## 0.04862602 9.616710 0.2030285 7.210699
## 0.05094138 9.613910 0.2034061 7.208763
## 0.05336699 9.611037 0.2037994 7.206782
## 0.05590810 9.608191 0.2041925 7.204807
## 0.05857021 9.605362 0.2045866 7.202842
## 0.06135907 9.602557 0.2049816 7.200901
## 0.06428073 9.599734 0.2053840 7.199000
## 0.06734151 9.596885 0.2057958 7.197076
## 0.07054802 9.594012 0.2062178 7.195183
## 0.07390722 9.591143 0.2066455 7.193257
## 0.07742637 9.588277 0.2070792 7.191382
## 0.08111308 9.585420 0.2075185 7.189601
## 0.08497534 9.582480 0.2079799 7.187666
## 0.08902151 9.579552 0.2084479 7.185716
## 0.09326033 9.576664 0.2089195 7.183743
## 0.09770100 9.573888 0.2093827 7.181969
## 0.10235310 9.571028 0.2098708 7.180108
## 0.10722672 9.568137 0.2103754 7.178367
## 0.11233240 9.565267 0.2108869 7.176642
## 0.11768120 9.562503 0.2113917 7.175070
## 0.12328467 9.559797 0.2118961 7.173592
## 0.12915497 9.557171 0.2123985 7.172267
## 0.13530478 9.554631 0.2128994 7.171060
## 0.14174742 9.552208 0.2133942 7.169982
## 0.14849683 9.549898 0.2138846 7.169145
## 0.15556761 9.547798 0.2143546 7.168613
## 0.16297508 9.545768 0.2148292 7.168227
## 0.17073526 9.544084 0.2152603 7.168232
## 0.17886495 9.542876 0.2156236 7.168706
## 0.18738174 9.542090 0.2159305 7.169562
## 0.19630407 9.541690 0.2161876 7.170711
## 0.20565123 9.541671 0.2163969 7.172152
## 0.21544347 9.542450 0.2164836 7.174521
## 0.22570197 9.543938 0.2164631 7.177692
## 0.23644894 9.546031 0.2163527 7.181313
## 0.24770764 9.548604 0.2161775 7.185329
## 0.25950242 9.551729 0.2159250 7.189818
## 0.27185882 9.555555 0.2155673 7.194719
## 0.28480359 9.559537 0.2152021 7.199686
## 0.29836472 9.563935 0.2147845 7.204897
## 0.31257158 9.568234 0.2144162 7.209862
## 0.32745492 9.572758 0.2140382 7.215102
## 0.34304693 9.577763 0.2136036 7.220779
## 0.35938137 9.583137 0.2131356 7.226737
## 0.37649358 9.588988 0.2126201 7.233139
## 0.39442061 9.595407 0.2120385 7.240133
## 0.41320124 9.602478 0.2113764 7.247595
## 0.43287613 9.610291 0.2106184 7.255678
## 0.45348785 9.618707 0.2097963 7.264254
## 0.47508102 9.627833 0.2088881 7.273393
## 0.49770236 9.637721 0.2078838 7.283147
## 0.52140083 9.648458 0.2067660 7.293731
## 0.54622772 9.659229 0.2057080 7.304629
## 0.57223677 9.670100 0.2046964 7.315799
## 0.59948425 9.680347 0.2039161 7.326409
## 0.62802914 9.690774 0.2031960 7.337287
## 0.65793322 9.701400 0.2025560 7.348618
## 0.68926121 9.712725 0.2018941 7.360563
## 0.72208090 9.724793 0.2012150 7.373089
## 0.75646333 9.737983 0.2004409 7.386501
## 0.79248290 9.752395 0.1995560 7.400899
## 0.83021757 9.768176 0.1985277 7.416493
## 0.86974900 9.785466 0.1973253 7.433198
## 0.91116276 9.804406 0.1959137 7.451203
## 0.95454846 9.825149 0.1942497 7.470689
## 1.00000000 9.847863 0.1922793 7.491584
##
## Tuning parameter 'alpha' was held constant at a value of 1
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were alpha = 1 and lambda = 0.2056512.
## alpha lambda
## 66 1 0.2056512
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD
## 1 1 0.01000000 9.683314 0.1947497 7.258914 0.3428747 0.02697976
## 2 1 0.01047616 9.682252 0.1948730 7.258121 0.3429267 0.02698519
## 3 1 0.01097499 9.681149 0.1950015 7.257291 0.3429803 0.02699024
## 4 1 0.01149757 9.680001 0.1951353 7.256432 0.3430384 0.02699556
## 5 1 0.01204504 9.678811 0.1952741 7.255538 0.3431006 0.02700054
## 6 1 0.01261857 9.677573 0.1954190 7.254600 0.3431676 0.02700570
## 7 1 0.01321941 9.676283 0.1955702 7.253624 0.3432384 0.02701140
## 8 1 0.01384886 9.674944 0.1957276 7.252616 0.3433116 0.02701721
## 9 1 0.01450829 9.673560 0.1958905 7.251587 0.3433855 0.02702349
## 10 1 0.01519911 9.672134 0.1960584 7.250540 0.3434646 0.02703095
## 11 1 0.01592283 9.670659 0.1962327 7.249462 0.3435378 0.02703835
## 12 1 0.01668101 9.669132 0.1964136 7.248342 0.3436182 0.02704626
## 13 1 0.01747528 9.667530 0.1966044 7.247163 0.3436981 0.02705531
## 14 1 0.01830738 9.665859 0.1968043 7.245919 0.3437797 0.02706389
## 15 1 0.01917910 9.664136 0.1970110 7.244639 0.3438593 0.02707277
## 16 1 0.02009233 9.662350 0.1972262 7.243311 0.3439391 0.02708245
## 17 1 0.02104904 9.660489 0.1974516 7.241930 0.3440021 0.02709217
## 18 1 0.02205131 9.658565 0.1976854 7.240499 0.3440522 0.02710123
## 19 1 0.02310130 9.656594 0.1979257 7.239038 0.3440932 0.02710909
## 20 1 0.02420128 9.654557 0.1981749 7.237552 0.3441382 0.02711809
## 21 1 0.02535364 9.652449 0.1984343 7.236054 0.3441944 0.02712687
## 22 1 0.02656088 9.650271 0.1987039 7.234512 0.3442562 0.02713518
## 23 1 0.02782559 9.647993 0.1989883 7.232919 0.3443512 0.02714496
## 24 1 0.02915053 9.645617 0.1992872 7.231258 0.3444578 0.02714988
## 25 1 0.03053856 9.643192 0.1995933 7.229531 0.3445703 0.02715834
## 26 1 0.03199267 9.640731 0.1999053 7.227750 0.3446695 0.02717460
## 27 1 0.03351603 9.638243 0.2002211 7.225966 0.3447911 0.02718700
## 28 1 0.03511192 9.635698 0.2005461 7.224152 0.3448995 0.02720112
## 29 1 0.03678380 9.633099 0.2008795 7.222309 0.3449703 0.02721138
## 30 1 0.03853529 9.630444 0.2012218 7.220415 0.3450263 0.02721701
## 31 1 0.04037017 9.627751 0.2015710 7.218525 0.3450760 0.02722023
## 32 1 0.04229243 9.625034 0.2019255 7.216609 0.3451659 0.02722364
## 33 1 0.04430621 9.622280 0.2022876 7.214684 0.3452297 0.02722084
## 34 1 0.04641589 9.619490 0.2026574 7.212712 0.3452618 0.02721546
## 35 1 0.04862602 9.616710 0.2030285 7.210699 0.3453656 0.02721373
## 36 1 0.05094138 9.613910 0.2034061 7.208763 0.3455013 0.02721496
## 37 1 0.05336699 9.611037 0.2037994 7.206782 0.3457420 0.02723865
## 38 1 0.05590810 9.608191 0.2041925 7.204807 0.3460512 0.02727352
## 39 1 0.05857021 9.605362 0.2045866 7.202842 0.3462917 0.02730190
## 40 1 0.06135907 9.602557 0.2049816 7.200901 0.3465149 0.02733094
## 41 1 0.06428073 9.599734 0.2053840 7.199000 0.3467736 0.02735571
## 42 1 0.06734151 9.596885 0.2057958 7.197076 0.3470965 0.02739050
## 43 1 0.07054802 9.594012 0.2062178 7.195183 0.3474795 0.02742859
## 44 1 0.07390722 9.591143 0.2066455 7.193257 0.3479090 0.02747505
## 45 1 0.07742637 9.588277 0.2070792 7.191382 0.3483240 0.02752012
## 46 1 0.08111308 9.585420 0.2075185 7.189601 0.3487391 0.02756506
## 47 1 0.08497534 9.582480 0.2079799 7.187666 0.3490634 0.02761346
## 48 1 0.08902151 9.579552 0.2084479 7.185716 0.3493420 0.02766029
## 49 1 0.09326033 9.576664 0.2089195 7.183743 0.3495814 0.02772191
## 50 1 0.09770100 9.573888 0.2093827 7.181969 0.3497741 0.02778427
## 51 1 0.10235310 9.571028 0.2098708 7.180108 0.3499936 0.02785740
## 52 1 0.10722672 9.568137 0.2103754 7.178367 0.3502520 0.02792019
## 53 1 0.11233240 9.565267 0.2108869 7.176642 0.3502716 0.02795991
## 54 1 0.11768120 9.562503 0.2113917 7.175070 0.3501395 0.02798127
## 55 1 0.12328467 9.559797 0.2118961 7.173592 0.3498136 0.02794991
## 56 1 0.12915497 9.557171 0.2123985 7.172267 0.3495071 0.02790368
## 57 1 0.13530478 9.554631 0.2128994 7.171060 0.3491542 0.02784109
## 58 1 0.14174742 9.552208 0.2133942 7.169982 0.3487650 0.02773892
## 59 1 0.14849683 9.549898 0.2138846 7.169145 0.3484433 0.02763878
## 60 1 0.15556761 9.547798 0.2143546 7.168613 0.3481018 0.02753719
## 61 1 0.16297508 9.545768 0.2148292 7.168227 0.3477000 0.02747200
## 62 1 0.17073526 9.544084 0.2152603 7.168232 0.3471891 0.02741308
## 63 1 0.17886495 9.542876 0.2156236 7.168706 0.3464664 0.02732299
## 64 1 0.18738174 9.542090 0.2159305 7.169562 0.3456185 0.02724017
## 65 1 0.19630407 9.541690 0.2161876 7.170711 0.3448662 0.02718254
## 66 1 0.20565123 9.541671 0.2163969 7.172152 0.3441831 0.02713016
## 67 1 0.21544347 9.542450 0.2164836 7.174521 0.3435270 0.02706044
## 68 1 0.22570197 9.543938 0.2164631 7.177692 0.3429665 0.02699680
## 69 1 0.23644894 9.546031 0.2163527 7.181313 0.3426665 0.02691512
## 70 1 0.24770764 9.548604 0.2161775 7.185329 0.3425309 0.02684046
## 71 1 0.25950242 9.551729 0.2159250 7.189818 0.3424788 0.02680263
## 72 1 0.27185882 9.555555 0.2155673 7.194719 0.3424559 0.02680931
## 73 1 0.28480359 9.559537 0.2152021 7.199686 0.3422290 0.02679738
## 74 1 0.29836472 9.563935 0.2147845 7.204897 0.3418398 0.02677481
## 75 1 0.31257158 9.568234 0.2144162 7.209862 0.3416273 0.02680684
## 76 1 0.32745492 9.572758 0.2140382 7.215102 0.3413470 0.02682275
## 77 1 0.34304693 9.577763 0.2136036 7.220779 0.3410178 0.02682982
## 78 1 0.35938137 9.583137 0.2131356 7.226737 0.3406714 0.02681620
## 79 1 0.37649358 9.588988 0.2126201 7.233139 0.3402374 0.02679919
## 80 1 0.39442061 9.595407 0.2120385 7.240133 0.3397507 0.02678886
## 81 1 0.41320124 9.602478 0.2113764 7.247595 0.3392553 0.02678000
## 82 1 0.43287613 9.610291 0.2106184 7.255678 0.3387778 0.02678562
## 83 1 0.45348785 9.618707 0.2097963 7.264254 0.3382744 0.02679074
## 84 1 0.47508102 9.627833 0.2088881 7.273393 0.3377416 0.02679681
## 85 1 0.49770236 9.637721 0.2078838 7.283147 0.3371289 0.02679664
## 86 1 0.52140083 9.648458 0.2067660 7.293731 0.3364154 0.02678696
## 87 1 0.54622772 9.659229 0.2057080 7.304629 0.3357770 0.02677232
## 88 1 0.57223677 9.670100 0.2046964 7.315799 0.3349302 0.02661834
## 89 1 0.59948425 9.680347 0.2039161 7.326409 0.3345857 0.02662485
## 90 1 0.62802914 9.690774 0.2031960 7.337287 0.3340211 0.02657944
## 91 1 0.65793322 9.701400 0.2025560 7.348618 0.3334371 0.02667713
## 92 1 0.68926121 9.712725 0.2018941 7.360563 0.3324971 0.02673405
## 93 1 0.72208090 9.724793 0.2012150 7.373089 0.3317698 0.02686315
## 94 1 0.75646333 9.737983 0.2004409 7.386501 0.3313057 0.02704626
## 95 1 0.79248290 9.752395 0.1995560 7.400899 0.3308204 0.02725166
## 96 1 0.83021757 9.768176 0.1985277 7.416493 0.3303270 0.02748130
## 97 1 0.86974900 9.785466 0.1973253 7.433198 0.3298203 0.02773901
## 98 1 0.91116276 9.804406 0.1959137 7.451203 0.3293017 0.02802852
## 99 1 0.95454846 9.825149 0.1942497 7.470689 0.3287731 0.02835385
## 100 1 1.00000000 9.847863 0.1922793 7.491584 0.3282368 0.02871917
## MAESD
## 1 0.1951676
## 2 0.1951837
## 3 0.1952010
## 4 0.1952238
## 5 0.1952524
## 6 0.1952842
## 7 0.1953180
## 8 0.1953605
## 9 0.1954075
## 10 0.1954552
## 11 0.1954845
## 12 0.1954986
## 13 0.1955090
## 14 0.1955179
## 15 0.1955392
## 16 0.1955751
## 17 0.1956029
## 18 0.1956323
## 19 0.1956673
## 20 0.1956956
## 21 0.1956733
## 22 0.1956454
## 23 0.1956382
## 24 0.1956443
## 25 0.1956761
## 26 0.1957274
## 27 0.1958033
## 28 0.1958443
## 29 0.1958324
## 30 0.1958208
## 31 0.1957562
## 32 0.1956984
## 33 0.1956896
## 34 0.1957049
## 35 0.1958062
## 36 0.1959310
## 37 0.1960475
## 38 0.1960717
## 39 0.1960146
## 40 0.1958644
## 41 0.1957417
## 42 0.1957212
## 43 0.1958509
## 44 0.1959891
## 45 0.1960083
## 46 0.1960946
## 47 0.1962168
## 48 0.1963759
## 49 0.1965028
## 50 0.1965934
## 51 0.1966812
## 52 0.1968328
## 53 0.1966829
## 54 0.1965463
## 55 0.1961633
## 56 0.1956906
## 57 0.1948314
## 58 0.1938750
## 59 0.1930786
## 60 0.1923940
## 61 0.1916089
## 62 0.1910437
## 63 0.1902613
## 64 0.1894573
## 65 0.1885676
## 66 0.1876295
## 67 0.1867392
## 68 0.1860179
## 69 0.1855288
## 70 0.1852457
## 71 0.1851124
## 72 0.1850899
## 73 0.1849618
## 74 0.1847224
## 75 0.1846318
## 76 0.1845475
## 77 0.1844374
## 78 0.1842587
## 79 0.1841053
## 80 0.1839147
## 81 0.1837462
## 82 0.1836985
## 83 0.1836315
## 84 0.1834965
## 85 0.1833816
## 86 0.1832064
## 87 0.1829106
## 88 0.1823745
## 89 0.1818319
## 90 0.1812989
## 91 0.1806567
## 92 0.1797544
## 93 0.1791612
## 94 0.1787535
## 95 0.1785088
## 96 0.1782305
## 97 0.1779180
## 98 0.1774256
## 99 0.1768560
## 100 0.1761699
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LASSO.caret == TRUE){
test.model(model.LASSO.caret, data.test
,method = 'glmnet',subopt = "LASSO"
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 111.2 122.3 125.4 125.4 128.7 139.3
## [1] "glmnet LASSO Test MSE: 92.838456849401"
LASSO with CV (w/ filtered train)
Train
if (algo.LASSO.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "glmnet"
,subopt = 'LASSO'
,feature.names = feature.names)
model.LASSO.caret = returned$model
}## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.102 on full training set
## glmnet
##
## 5714 samples
## 240 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5142, 5142, 5143, 5143, 5143, 5143, ...
## Resampling results across tuning parameters:
##
## lambda RMSE Rsquared MAE
## 0.01000000 7.464449 0.3034518 6.004609
## 0.01047616 7.463527 0.3035824 6.003898
## 0.01097499 7.462571 0.3037181 6.003163
## 0.01149757 7.461579 0.3038592 6.002399
## 0.01204504 7.460558 0.3040043 6.001614
## 0.01261857 7.459502 0.3041546 6.000800
## 0.01321941 7.458417 0.3043093 5.999965
## 0.01384886 7.457296 0.3044694 5.999116
## 0.01450829 7.456149 0.3046334 5.998246
## 0.01519911 7.454970 0.3048025 5.997346
## 0.01592283 7.453775 0.3049739 5.996445
## 0.01668101 7.452541 0.3051515 5.995511
## 0.01747528 7.451268 0.3053355 5.994569
## 0.01830738 7.449957 0.3055258 5.993594
## 0.01917910 7.448604 0.3057230 5.992612
## 0.02009233 7.447217 0.3059258 5.991626
## 0.02104904 7.445803 0.3061331 5.990635
## 0.02205131 7.444360 0.3063453 5.989632
## 0.02310130 7.442886 0.3065625 5.988628
## 0.02420128 7.441380 0.3067852 5.987606
## 0.02535364 7.439884 0.3070063 5.986590
## 0.02656088 7.438367 0.3072313 5.985547
## 0.02782559 7.436823 0.3074619 5.984460
## 0.02915053 7.435255 0.3076971 5.983371
## 0.03053856 7.433606 0.3079470 5.982248
## 0.03199267 7.431923 0.3082040 5.981112
## 0.03351603 7.430213 0.3084672 5.980009
## 0.03511192 7.428494 0.3087333 5.978877
## 0.03678380 7.426791 0.3089977 5.977699
## 0.03853529 7.425091 0.3092638 5.976526
## 0.04037017 7.423354 0.3095392 5.975334
## 0.04229243 7.421628 0.3098156 5.974183
## 0.04430621 7.419898 0.3100962 5.973065
## 0.04641589 7.418196 0.3103751 5.972014
## 0.04862602 7.416550 0.3106469 5.971039
## 0.05094138 7.414952 0.3109142 5.970117
## 0.05336699 7.413430 0.3111725 5.969324
## 0.05590810 7.411988 0.3114207 5.968709
## 0.05857021 7.410594 0.3116656 5.968206
## 0.06135907 7.409276 0.3119025 5.967867
## 0.06428073 7.407996 0.3121382 5.967566
## 0.06734151 7.406805 0.3123644 5.967352
## 0.07054802 7.405576 0.3126054 5.966991
## 0.07390722 7.404463 0.3128324 5.966643
## 0.07742637 7.403422 0.3130540 5.966333
## 0.08111308 7.402504 0.3132617 5.966059
## 0.08497534 7.401438 0.3135085 5.965552
## 0.08902151 7.400547 0.3137330 5.965180
## 0.09326033 7.399878 0.3139259 5.964900
## 0.09770100 7.399452 0.3140836 5.964749
## 0.10235310 7.399278 0.3142043 5.964759
## 0.10722672 7.399386 0.3142820 5.965066
## 0.11233240 7.399776 0.3143163 5.965509
## 0.11768120 7.400489 0.3143000 5.966081
## 0.12328467 7.401288 0.3142792 5.966719
## 0.12915497 7.402379 0.3142142 5.967652
## 0.13530478 7.403522 0.3141548 5.968801
## 0.14174742 7.404977 0.3140470 5.970301
## 0.14849683 7.406445 0.3139501 5.971794
## 0.15556761 7.408281 0.3137950 5.973572
## 0.16297508 7.410457 0.3135858 5.975574
## 0.17073526 7.413142 0.3132906 5.977999
## 0.17886495 7.416022 0.3129705 5.980744
## 0.18738174 7.419410 0.3125661 5.983762
## 0.19630407 7.423176 0.3121025 5.986993
## 0.20565123 7.427342 0.3115776 5.990519
## 0.21544347 7.431557 0.3110759 5.993916
## 0.22570197 7.436253 0.3104992 5.997585
## 0.23644894 7.441064 0.3099320 6.001454
## 0.24770764 7.446356 0.3092923 6.005923
## 0.25950242 7.451776 0.3086593 6.010998
## 0.27185882 7.457606 0.3079705 6.016452
## 0.28480359 7.463349 0.3073312 6.021865
## 0.29836472 7.469583 0.3066248 6.027712
## 0.31257158 7.476067 0.3059119 6.033847
## 0.32745492 7.483209 0.3051021 6.040534
## 0.34304693 7.491082 0.3041778 6.047657
## 0.35938137 7.499611 0.3031639 6.055306
## 0.37649358 7.508558 0.3021237 6.063291
## 0.39442061 7.518159 0.3009982 6.071765
## 0.41320124 7.527725 0.2999492 6.080219
## 0.43287613 7.538147 0.2987751 6.089350
## 0.45348785 7.549132 0.2975475 6.098878
## 0.47508102 7.561098 0.2961721 6.109131
## 0.49770236 7.574111 0.2946375 6.120190
## 0.52140083 7.588144 0.2929471 6.132113
## 0.54622772 7.602226 0.2913395 6.144068
## 0.57223677 7.617139 0.2896297 6.156764
## 0.59948425 7.632158 0.2880013 6.169387
## 0.62802914 7.648292 0.2862101 6.182957
## 0.65793322 7.664646 0.2844942 6.196976
## 0.68926121 7.682064 0.2826550 6.211769
## 0.72208090 7.698666 0.2812269 6.225881
## 0.75646333 7.716358 0.2797040 6.240754
## 0.79248290 7.734477 0.2782931 6.256334
## 0.83021757 7.754216 0.2766752 6.273329
## 0.86974900 7.775774 0.2747959 6.291540
## 0.91116276 7.799365 0.2725869 6.311228
## 0.95454846 7.825172 0.2699797 6.332677
## 1.00000000 7.853396 0.2668897 6.355823
##
## Tuning parameter 'alpha' was held constant at a value of 1
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were alpha = 1 and lambda = 0.1023531.
## alpha lambda
## 51 1 0.1023531
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD
## 1 1 0.01000000 7.464449 0.3034518 6.004609 0.1823117 0.02938006
## 2 1 0.01047616 7.463527 0.3035824 6.003898 0.1823606 0.02940169
## 3 1 0.01097499 7.462571 0.3037181 6.003163 0.1824108 0.02942529
## 4 1 0.01149757 7.461579 0.3038592 6.002399 0.1824634 0.02945040
## 5 1 0.01204504 7.460558 0.3040043 6.001614 0.1825086 0.02947758
## 6 1 0.01261857 7.459502 0.3041546 6.000800 0.1825530 0.02950639
## 7 1 0.01321941 7.458417 0.3043093 5.999965 0.1825982 0.02953661
## 8 1 0.01384886 7.457296 0.3044694 5.999116 0.1826443 0.02956821
## 9 1 0.01450829 7.456149 0.3046334 5.998246 0.1826881 0.02960123
## 10 1 0.01519911 7.454970 0.3048025 5.997346 0.1827300 0.02963513
## 11 1 0.01592283 7.453775 0.3049739 5.996445 0.1827699 0.02966987
## 12 1 0.01668101 7.452541 0.3051515 5.995511 0.1828114 0.02970680
## 13 1 0.01747528 7.451268 0.3053355 5.994569 0.1828329 0.02974460
## 14 1 0.01830738 7.449957 0.3055258 5.993594 0.1828511 0.02978421
## 15 1 0.01917910 7.448604 0.3057230 5.992612 0.1828504 0.02982308
## 16 1 0.02009233 7.447217 0.3059258 5.991626 0.1828436 0.02986228
## 17 1 0.02104904 7.445803 0.3061331 5.990635 0.1828364 0.02990182
## 18 1 0.02205131 7.444360 0.3063453 5.989632 0.1828315 0.02994339
## 19 1 0.02310130 7.442886 0.3065625 5.988628 0.1828431 0.02998457
## 20 1 0.02420128 7.441380 0.3067852 5.987606 0.1828583 0.03002489
## 21 1 0.02535364 7.439884 0.3070063 5.986590 0.1828744 0.03006408
## 22 1 0.02656088 7.438367 0.3072313 5.985547 0.1828817 0.03010398
## 23 1 0.02782559 7.436823 0.3074619 5.984460 0.1828611 0.03014051
## 24 1 0.02915053 7.435255 0.3076971 5.983371 0.1828382 0.03017851
## 25 1 0.03053856 7.433606 0.3079470 5.982248 0.1828746 0.03021809
## 26 1 0.03199267 7.431923 0.3082040 5.981112 0.1829263 0.03025847
## 27 1 0.03351603 7.430213 0.3084672 5.980009 0.1830048 0.03030471
## 28 1 0.03511192 7.428494 0.3087333 5.978877 0.1830902 0.03035434
## 29 1 0.03678380 7.426791 0.3089977 5.977699 0.1831859 0.03040740
## 30 1 0.03853529 7.425091 0.3092638 5.976526 0.1832831 0.03046708
## 31 1 0.04037017 7.423354 0.3095392 5.975334 0.1833731 0.03053253
## 32 1 0.04229243 7.421628 0.3098156 5.974183 0.1834686 0.03060361
## 33 1 0.04430621 7.419898 0.3100962 5.973065 0.1835397 0.03067942
## 34 1 0.04641589 7.418196 0.3103751 5.972014 0.1836275 0.03076035
## 35 1 0.04862602 7.416550 0.3106469 5.971039 0.1836975 0.03084000
## 36 1 0.05094138 7.414952 0.3109142 5.970117 0.1837804 0.03092564
## 37 1 0.05336699 7.413430 0.3111725 5.969324 0.1838640 0.03101066
## 38 1 0.05590810 7.411988 0.3114207 5.968709 0.1839565 0.03109628
## 39 1 0.05857021 7.410594 0.3116656 5.968206 0.1841148 0.03117669
## 40 1 0.06135907 7.409276 0.3119025 5.967867 0.1843067 0.03125984
## 41 1 0.06428073 7.407996 0.3121382 5.967566 0.1844550 0.03134515
## 42 1 0.06734151 7.406805 0.3123644 5.967352 0.1846283 0.03143565
## 43 1 0.07054802 7.405576 0.3126054 5.966991 0.1849114 0.03152103
## 44 1 0.07390722 7.404463 0.3128324 5.966643 0.1852236 0.03160909
## 45 1 0.07742637 7.403422 0.3130540 5.966333 0.1857055 0.03172026
## 46 1 0.08111308 7.402504 0.3132617 5.966059 0.1862178 0.03183648
## 47 1 0.08497534 7.401438 0.3135085 5.965552 0.1866739 0.03195561
## 48 1 0.08902151 7.400547 0.3137330 5.965180 0.1871606 0.03206951
## 49 1 0.09326033 7.399878 0.3139259 5.964900 0.1876182 0.03217063
## 50 1 0.09770100 7.399452 0.3140836 5.964749 0.1881332 0.03226223
## 51 1 0.10235310 7.399278 0.3142043 5.964759 0.1885725 0.03231641
## 52 1 0.10722672 7.399386 0.3142820 5.965066 0.1890776 0.03236830
## 53 1 0.11233240 7.399776 0.3143163 5.965509 0.1897822 0.03243721
## 54 1 0.11768120 7.400489 0.3143000 5.966081 0.1905579 0.03251459
## 55 1 0.12328467 7.401288 0.3142792 5.966719 0.1914794 0.03261423
## 56 1 0.12915497 7.402379 0.3142142 5.967652 0.1924716 0.03271497
## 57 1 0.13530478 7.403522 0.3141548 5.968801 0.1931947 0.03278157
## 58 1 0.14174742 7.404977 0.3140470 5.970301 0.1939614 0.03285622
## 59 1 0.14849683 7.406445 0.3139501 5.971794 0.1948310 0.03297872
## 60 1 0.15556761 7.408281 0.3137950 5.973572 0.1957634 0.03309324
## 61 1 0.16297508 7.410457 0.3135858 5.975574 0.1966061 0.03319619
## 62 1 0.17073526 7.413142 0.3132906 5.977999 0.1974188 0.03328370
## 63 1 0.17886495 7.416022 0.3129705 5.980744 0.1979886 0.03330405
## 64 1 0.18738174 7.419410 0.3125661 5.983762 0.1985552 0.03331806
## 65 1 0.19630407 7.423176 0.3121025 5.986993 0.1989084 0.03328917
## 66 1 0.20565123 7.427342 0.3115776 5.990519 0.1993589 0.03328486
## 67 1 0.21544347 7.431557 0.3110759 5.993916 0.1998382 0.03333961
## 68 1 0.22570197 7.436253 0.3104992 5.997585 0.2004985 0.03340102
## 69 1 0.23644894 7.441064 0.3099320 6.001454 0.2014673 0.03345638
## 70 1 0.24770764 7.446356 0.3092923 6.005923 0.2024712 0.03348147
## 71 1 0.25950242 7.451776 0.3086593 6.010998 0.2032402 0.03343499
## 72 1 0.27185882 7.457606 0.3079705 6.016452 0.2039853 0.03337921
## 73 1 0.28480359 7.463349 0.3073312 6.021865 0.2038948 0.03321952
## 74 1 0.29836472 7.469583 0.3066248 6.027712 0.2038513 0.03304578
## 75 1 0.31257158 7.476067 0.3059119 6.033847 0.2040406 0.03290499
## 76 1 0.32745492 7.483209 0.3051021 6.040534 0.2042943 0.03274038
## 77 1 0.34304693 7.491082 0.3041778 6.047657 0.2046334 0.03256129
## 78 1 0.35938137 7.499611 0.3031639 6.055306 0.2050452 0.03239436
## 79 1 0.37649358 7.508558 0.3021237 6.063291 0.2057133 0.03232777
## 80 1 0.39442061 7.518159 0.3009982 6.071765 0.2064371 0.03227731
## 81 1 0.41320124 7.527725 0.2999492 6.080219 0.2068702 0.03225363
## 82 1 0.43287613 7.538147 0.2987751 6.089350 0.2073673 0.03223544
## 83 1 0.45348785 7.549132 0.2975475 6.098878 0.2081198 0.03225696
## 84 1 0.47508102 7.561098 0.2961721 6.109131 0.2089836 0.03226808
## 85 1 0.49770236 7.574111 0.2946375 6.120190 0.2100132 0.03228237
## 86 1 0.52140083 7.588144 0.2929471 6.132113 0.2110386 0.03234454
## 87 1 0.54622772 7.602226 0.2913395 6.144068 0.2116355 0.03238539
## 88 1 0.57223677 7.617139 0.2896297 6.156764 0.2122962 0.03240501
## 89 1 0.59948425 7.632158 0.2880013 6.169387 0.2126796 0.03233211
## 90 1 0.62802914 7.648292 0.2862101 6.182957 0.2130703 0.03223548
## 91 1 0.65793322 7.664646 0.2844942 6.196976 0.2132749 0.03215346
## 92 1 0.68926121 7.682064 0.2826550 6.211769 0.2134535 0.03207349
## 93 1 0.72208090 7.698666 0.2812269 6.225881 0.2133113 0.03206962
## 94 1 0.75646333 7.716358 0.2797040 6.240754 0.2133166 0.03203257
## 95 1 0.79248290 7.734477 0.2782931 6.256334 0.2134687 0.03206183
## 96 1 0.83021757 7.754216 0.2766752 6.273329 0.2136688 0.03206200
## 97 1 0.86974900 7.775774 0.2747959 6.291540 0.2139198 0.03206281
## 98 1 0.91116276 7.799365 0.2725869 6.311228 0.2141975 0.03205742
## 99 1 0.95454846 7.825172 0.2699797 6.332677 0.2145037 0.03204387
## 100 1 1.00000000 7.853396 0.2668897 6.355823 0.2148399 0.03201967
## MAESD
## 1 0.1616226
## 2 0.1616612
## 3 0.1616981
## 4 0.1617330
## 5 0.1617508
## 6 0.1617626
## 7 0.1617733
## 8 0.1617788
## 9 0.1617818
## 10 0.1617772
## 11 0.1617691
## 12 0.1617621
## 13 0.1617442
## 14 0.1617225
## 15 0.1617016
## 16 0.1616866
## 17 0.1616977
## 18 0.1617166
## 19 0.1617523
## 20 0.1617864
## 21 0.1618035
## 22 0.1618120
## 23 0.1618090
## 24 0.1617952
## 25 0.1618520
## 26 0.1618652
## 27 0.1618657
## 28 0.1618888
## 29 0.1619270
## 30 0.1619444
## 31 0.1619948
## 32 0.1620474
## 33 0.1621043
## 34 0.1621959
## 35 0.1622381
## 36 0.1622381
## 37 0.1621995
## 38 0.1621305
## 39 0.1620621
## 40 0.1619294
## 41 0.1617460
## 42 0.1615430
## 43 0.1614585
## 44 0.1614503
## 45 0.1615903
## 46 0.1618176
## 47 0.1620445
## 48 0.1622738
## 49 0.1625039
## 50 0.1627787
## 51 0.1630008
## 52 0.1631881
## 53 0.1634693
## 54 0.1638669
## 55 0.1642908
## 56 0.1646584
## 57 0.1646686
## 58 0.1646377
## 59 0.1644920
## 60 0.1642434
## 61 0.1640613
## 62 0.1638621
## 63 0.1634971
## 64 0.1630899
## 65 0.1623659
## 66 0.1617647
## 67 0.1612401
## 68 0.1608198
## 69 0.1606990
## 70 0.1604436
## 71 0.1601182
## 72 0.1596238
## 73 0.1584147
## 74 0.1572837
## 75 0.1561295
## 76 0.1549931
## 77 0.1538531
## 78 0.1528145
## 79 0.1522349
## 80 0.1516859
## 81 0.1509999
## 82 0.1503580
## 83 0.1499753
## 84 0.1496719
## 85 0.1495059
## 86 0.1491742
## 87 0.1483958
## 88 0.1478541
## 89 0.1473155
## 90 0.1467687
## 91 0.1458654
## 92 0.1451282
## 93 0.1444076
## 94 0.1436026
## 95 0.1427448
## 96 0.1418765
## 97 0.1411443
## 98 0.1403082
## 99 0.1394692
## 100 0.1387076
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LASSO.caret == TRUE){
test.model(model.LASSO.caret, data.test
,method = 'glmnet',subopt = "LASSO"
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 108.2 121.0 124.3 124.2 127.7 137.8
## [1] "glmnet LASSO Test MSE: 94.1983462379039"
LARS with CV (w/ full train)
Train
if (algo.LARS.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "lars"
,subopt = 'NULL'
,feature.names = feature.names)
model.LARS.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting fraction = 0.333 on full training set
## Least Angle Regression
##
## 6002 samples
## 240 predictor
##
## Pre-processing: centered (240), scaled (240)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5402, 5401, 5402, 5401, 5402, 5402, ...
## Resampling results across tuning parameters:
##
## fraction RMSE Rsquared MAE
## 0.00000000 10.760090 NaN 8.200318
## 0.01010101 10.644307 0.1018765 8.116711
## 0.02020202 10.541440 0.1018765 8.043834
## 0.03030303 10.451868 0.1018765 7.979406
## 0.04040404 10.374603 0.1153701 7.923138
## 0.05050505 10.303565 0.1259936 7.870798
## 0.06060606 10.239769 0.1382632 7.821010
## 0.07070707 10.178252 0.1505818 7.771863
## 0.08080808 10.119445 0.1607048 7.724405
## 0.09090909 10.064007 0.1686065 7.679384
## 0.10101010 10.012462 0.1747299 7.636412
## 0.11111111 9.965469 0.1797679 7.595599
## 0.12121212 9.920291 0.1850407 7.556006
## 0.13131313 9.877367 0.1896411 7.518051
## 0.14141414 9.837288 0.1933784 7.481677
## 0.15151515 9.800091 0.1964029 7.446967
## 0.16161616 9.765809 0.1988408 7.414052
## 0.17171717 9.734471 0.2007969 7.382789
## 0.18181818 9.707031 0.2023281 7.354420
## 0.19191919 9.683714 0.2037882 7.329798
## 0.20202020 9.662740 0.2055124 7.308552
## 0.21212121 9.642596 0.2075154 7.288499
## 0.22222222 9.623261 0.2094977 7.269333
## 0.23232323 9.605266 0.2112882 7.251097
## 0.24242424 9.589823 0.2127114 7.234592
## 0.25252525 9.577284 0.2137853 7.220741
## 0.26262626 9.567319 0.2145960 7.209203
## 0.27272727 9.559074 0.2153219 7.199484
## 0.28282828 9.552480 0.2159356 7.191107
## 0.29292929 9.547636 0.2163381 7.184513
## 0.30303030 9.544099 0.2165834 7.178942
## 0.31313131 9.542030 0.2166326 7.174769
## 0.32323232 9.540843 0.2165788 7.171796
## 0.33333333 9.540558 0.2164054 7.169875
## 0.34343434 9.540949 0.2161423 7.168824
## 0.35353535 9.541685 0.2158501 7.168143
## 0.36363636 9.542771 0.2155205 7.167839
## 0.37373737 9.544135 0.2151582 7.167769
## 0.38383838 9.545849 0.2147481 7.167892
## 0.39393939 9.547581 0.2143498 7.168248
## 0.40404040 9.549239 0.2139802 7.168613
## 0.41414141 9.551035 0.2136003 7.169307
## 0.42424242 9.552932 0.2132138 7.170100
## 0.43434343 9.554817 0.2128378 7.170987
## 0.44444444 9.556632 0.2124806 7.171901
## 0.45454545 9.558571 0.2121098 7.172922
## 0.46464646 9.560609 0.2117300 7.173981
## 0.47474747 9.562570 0.2113693 7.175026
## 0.48484848 9.564582 0.2110048 7.176110
## 0.49494949 9.566544 0.2106523 7.177276
## 0.50505051 9.568559 0.2102970 7.178474
## 0.51515152 9.570615 0.2099403 7.179766
## 0.52525253 9.572593 0.2096007 7.181126
## 0.53535354 9.574487 0.2092799 7.182359
## 0.54545455 9.576411 0.2089592 7.183627
## 0.55555556 9.578445 0.2086246 7.185047
## 0.56565657 9.580555 0.2082827 7.186450
## 0.57575758 9.582658 0.2079475 7.187848
## 0.58585859 9.584794 0.2076118 7.189209
## 0.59595960 9.586900 0.2072845 7.190513
## 0.60606061 9.588995 0.2069631 7.191850
## 0.61616162 9.591098 0.2066453 7.193318
## 0.62626263 9.593307 0.2063147 7.194813
## 0.63636364 9.595596 0.2059760 7.196310
## 0.64646465 9.597862 0.2056447 7.197741
## 0.65656566 9.600103 0.2053224 7.199216
## 0.66666667 9.602319 0.2050078 7.200715
## 0.67676768 9.604642 0.2046808 7.202315
## 0.68686869 9.607088 0.2043382 7.203996
## 0.69696970 9.609600 0.2039894 7.205732
## 0.70707071 9.612191 0.2036323 7.207517
## 0.71717172 9.614767 0.2032820 7.209286
## 0.72727273 9.617403 0.2029275 7.211150
## 0.73737374 9.620087 0.2025700 7.213064
## 0.74747475 9.622847 0.2022054 7.214997
## 0.75757576 9.625664 0.2018366 7.216956
## 0.76767677 9.628562 0.2014593 7.219024
## 0.77777778 9.631507 0.2010795 7.221127
## 0.78787879 9.634541 0.2006898 7.223299
## 0.79797980 9.637578 0.2003024 7.225438
## 0.80808081 9.640655 0.1999123 7.227661
## 0.81818182 9.643788 0.1995173 7.229918
## 0.82828283 9.647044 0.1991070 7.232232
## 0.83838384 9.650297 0.1987014 7.234497
## 0.84848485 9.653542 0.1983013 7.236787
## 0.85858586 9.656778 0.1979059 7.239121
## 0.86868687 9.660096 0.1975018 7.241596
## 0.87878788 9.663445 0.1970971 7.244080
## 0.88888889 9.666838 0.1966901 7.246618
## 0.89898990 9.670262 0.1962825 7.249156
## 0.90909091 9.673715 0.1958745 7.251690
## 0.91919192 9.677250 0.1954585 7.254341
## 0.92929293 9.680834 0.1950394 7.257034
## 0.93939394 9.684472 0.1946168 7.259750
## 0.94949495 9.688139 0.1941937 7.262483
## 0.95959596 9.691820 0.1937723 7.265207
## 0.96969697 9.695535 0.1933504 7.267965
## 0.97979798 9.699277 0.1929287 7.270793
## 0.98989899 9.703077 0.1925023 7.273705
## 1.00000000 9.706892 0.1920777 7.276628
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was fraction = 0.3333333.
## fraction
## 34 0.3333333## Warning: Removed 1 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LARS.caret == TRUE){
test.model(model.LARS.caret, data.test
,method = 'lars',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 111.2 122.3 125.4 125.4 128.7 139.3
## [1] "lars Test MSE: 92.8032533211613"
LARS with CV (w/ filtered train)
Train
if (algo.LARS.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "lars"
,subopt = 'NULL'
,feature.names = feature.names)
model.LARS.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting fraction = 0.505 on full training set
## Least Angle Regression
##
## 5714 samples
## 240 predictor
##
## Pre-processing: centered (240), scaled (240)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5142, 5142, 5143, 5143, 5143, 5143, ...
## Resampling results across tuning parameters:
##
## fraction RMSE Rsquared MAE
## 0.00000000 8.923500 NaN 7.126370
## 0.01010101 8.791002 0.1410643 7.033091
## 0.02020202 8.672467 0.1410643 6.951806
## 0.03030303 8.570111 0.1450071 6.881497
## 0.04040404 8.477044 0.1680887 6.817882
## 0.05050505 8.390403 0.1825041 6.759232
## 0.06060606 8.311800 0.1915863 6.706277
## 0.07070707 8.240655 0.2058563 6.654840
## 0.08080808 8.171385 0.2206433 6.603761
## 0.09090909 8.105831 0.2323555 6.554359
## 0.10101010 8.044086 0.2415519 6.506722
## 0.11111111 7.987294 0.2492004 6.462118
## 0.12121212 7.934127 0.2566473 6.420588
## 0.13131313 7.882965 0.2634141 6.380017
## 0.14141414 7.834983 0.2689590 6.341073
## 0.15151515 7.790289 0.2734659 6.303893
## 0.16161616 7.748940 0.2771131 6.269106
## 0.17171717 7.711362 0.2800790 6.236754
## 0.18181818 7.678372 0.2830599 6.208614
## 0.19191919 7.647721 0.2863303 6.182525
## 0.20202020 7.619882 0.2894044 6.159149
## 0.21212121 7.593955 0.2922700 6.137084
## 0.22222222 7.568885 0.2952971 6.115827
## 0.23232323 7.546341 0.2978523 6.096510
## 0.24242424 7.526616 0.3000281 6.079271
## 0.25252525 7.508700 0.3020557 6.063574
## 0.26262626 7.492110 0.3040261 6.048681
## 0.27272727 7.478099 0.3056675 6.035932
## 0.28282828 7.466784 0.3069120 6.025210
## 0.29292929 7.457072 0.3080193 6.016047
## 0.30303030 7.448614 0.3090141 6.008069
## 0.31313131 7.441221 0.3099070 6.001365
## 0.32323232 7.434996 0.3106478 5.996494
## 0.33333333 7.429508 0.3113192 5.992302
## 0.34343434 7.424654 0.3119176 5.988401
## 0.35353535 7.420160 0.3124862 5.984594
## 0.36363636 7.416303 0.3129600 5.981081
## 0.37373737 7.412987 0.3133554 5.978048
## 0.38383838 7.410422 0.3136275 5.975765
## 0.39393939 7.408246 0.3138454 5.973816
## 0.40404040 7.406441 0.3140124 5.972127
## 0.41414141 7.405092 0.3141038 5.970753
## 0.42424242 7.404010 0.3141616 5.969635
## 0.43434343 7.402923 0.3142349 5.968502
## 0.44444444 7.401939 0.3142995 5.967502
## 0.45454545 7.401197 0.3143305 5.966716
## 0.46464646 7.400519 0.3143615 5.966079
## 0.47474747 7.399962 0.3143748 5.965699
## 0.48484848 7.399542 0.3143687 5.965358
## 0.49494949 7.399159 0.3143628 5.964959
## 0.50505051 7.398980 0.3143226 5.964675
## 0.51515152 7.399022 0.3142468 5.964540
## 0.52525253 7.399173 0.3141554 5.964563
## 0.53535354 7.399414 0.3140526 5.964674
## 0.54545455 7.399790 0.3139295 5.964855
## 0.55555556 7.400289 0.3137898 5.965050
## 0.56565657 7.400939 0.3136262 5.965315
## 0.57575758 7.401676 0.3134521 5.965640
## 0.58585859 7.402446 0.3132768 5.965952
## 0.59595960 7.403233 0.3131032 5.966264
## 0.60606061 7.403964 0.3129460 5.966426
## 0.61616162 7.404814 0.3127716 5.966711
## 0.62626263 7.405748 0.3125840 5.967004
## 0.63636364 7.406689 0.3124000 5.967234
## 0.64646465 7.407706 0.3122073 5.967487
## 0.65656566 7.408701 0.3120229 5.967663
## 0.66666667 7.409738 0.3118342 5.967859
## 0.67676768 7.410890 0.3116303 5.968204
## 0.68686869 7.412167 0.3114072 5.968702
## 0.69696970 7.413501 0.3111782 5.969318
## 0.70707071 7.414893 0.3109422 5.970052
## 0.71717172 7.416417 0.3106854 5.970927
## 0.72727273 7.417995 0.3104230 5.971867
## 0.73737374 7.419675 0.3101469 5.972883
## 0.74747475 7.421416 0.3098629 5.973986
## 0.75757576 7.423226 0.3095712 5.975202
## 0.76767677 7.425058 0.3092808 5.976469
## 0.77777778 7.426965 0.3089810 5.977812
## 0.78787879 7.428943 0.3086735 5.979155
## 0.79797980 7.431021 0.3083517 5.980512
## 0.80808081 7.433169 0.3080218 5.981940
## 0.81818182 7.435319 0.3076948 5.983413
## 0.82828283 7.437487 0.3073681 5.984931
## 0.83838384 7.439673 0.3070426 5.986451
## 0.84848485 7.441973 0.3067014 5.988008
## 0.85858586 7.444367 0.3063476 5.989651
## 0.86868687 7.446835 0.3059851 5.991359
## 0.87878788 7.449409 0.3056080 5.993203
## 0.88888889 7.452016 0.3052302 5.995139
## 0.89898990 7.454678 0.3048475 5.997139
## 0.90909091 7.457467 0.3044475 5.999248
## 0.91919192 7.460365 0.3040335 6.001467
## 0.92929293 7.463353 0.3036083 6.003766
## 0.93939394 7.466395 0.3031779 6.006162
## 0.94949495 7.469490 0.3027423 6.008644
## 0.95959596 7.472644 0.3023009 6.011197
## 0.96969697 7.475912 0.3018441 6.013844
## 0.97979798 7.479256 0.3013781 6.016565
## 0.98989899 7.482647 0.3009085 6.019337
## 1.00000000 7.486026 0.3004453 6.022099
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was fraction = 0.5050505.
## fraction
## 51 0.5050505## Warning: Removed 1 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LARS.caret == TRUE){
test.model(model.LARS.caret, data.test
,method = 'lars',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 108.2 121.0 124.3 124.2 127.7 137.8
## [1] "lars Test MSE: 94.1798045136605"